15 Multi Agent Systems Jobs in Noida

Location: GurgaonFunction: EngineeringReports to: CTOTeam size: 78 engineers (startup pod) Why this role Were building enterprisegrade Agentic AI platform & applications for recruitmentfrom sourcing and screening to interview assistance and offer orchestration. Youll lead a small, highleverage team that ships fast, measures rigorously, and scales responsibly. What youll do Own delivery endtoend: backlog, execution, quality, and timelines for Agentic AI features. Be handson (3050% coding): set the technical bar in Python/TypeScript; review PRs; unblock tricky problems. Design agentic systems: tooluse orchestration, planning/looping, memory, safety rails, and cost/perf optimization. Leverage LLMs smartly: RAG, structured output, function/tool calling, multimodel routing; evaluate build vs. buy. Ship production ML/LLM workflows: data pipelines, feature stores, vector indexes, retrievers, model registries. MLOps & Observability: automate training/inference CI/CD; monitor quality, drift, toxicity, latency, cost, and usage. EVALs & quality: define tasklevel metrics; set up offline/online EVALs (goldens, rubrics, humanintheloop) and guardrails. DevOps (Tshaped): own pragmatic infra with the teamGitHub Actions, containers, IaC, basic K8s; keep prod healthy. Security & compliance: enforce data privacy, tenancy isolation, PII handling; partner with Security for audits. People leadership: recruit, coach, and grow a hightrust team; establish rituals (standups, planning, postmortems). Stakeholder management: partner with Product/Design/Recruitment SMEs; translate business goals into roadmaps. What youve done (musthaves) 10+ years in software/ML; 4+ years leading engineers (TL/EM) in highvelocity product teams. Built and operated LLMpowered or ML products at scale (userfacing or enterprise workflows). Strong coding in Python, Java and TypeScript/Node; solid system design and API fundamentals. Exposure to frontend technologies like React, Angular, Flutter Experience on SQL databases like Postgres, MariaDB Practical MLOps: experiment tracking, model registries, reproducible training, feature/vectors, A/B rollouts. LLM tooling: orchestration (LangChain/LlamaIndex/DSPy), vector DBs (pgvector/FAISS/Pinecone/Weaviate), RAG patterns, context engineering Observability & EVALs: ML/LLM monitoring, LLM eval frameworks (RAGAS/DeepEval/OpenAI Evals), offline+online testing and human review. Comfortable with DevOps: GitHub Actions, Docker, basic Kubernetes, IaC (Terraform), and one major cloud (GCP/AWS/Azure). Familiar with AI SDLC tools: GitHub Copilot, Cursor, Claude Code, Code Llama/Codexstyle tools; test automation. Product mindset: measure outcomes (quality, cost, speed), not just outputs; datadriven decisions. Nice to have HRTech/recruitment domain (ATS/CRM, assessments, interview orchestration). Retrieval quality tuning, promptengineering at scale, policy/guardrail systems (OpenAI/Guardrails/NeMo Guardrails). Knowledge of multiagent frameworks, graph planners, or workflow engines (Prefect/Temporal). Experience with privacy preserving ML, tenancy isolation, regionalization.

We are hiring a Gen AI Architect to design intelligent, agentic systems on Google Cloud. Requires expertise in Python, Prompt Engineering, LangChain, LangGraph, Semantic Kernel, Gemini 2.5, multi-agent orchestration, LLMs, & scalable AI architecture.

As an AI Architect specializing in Agentic AI and Generative AI, your primary responsibility will be to design, develop, and deploy cutting-edge autonomous AI systems. You will focus on building LLM-powered agents with memory, tool usage, planning, and reasoning capabilities to create intelligent, goal-oriented systems. Your role as a technical leader will involve overseeing AI initiatives from research and architecture design to deployment in cloud environments. Key Responsibilities: - Architect and construct LLM-based agents capable of autonomous task execution, memory management, tool usage, and multi-step reasoning. - Develop modular, goal-oriented agentic systems utilizing tools like LangChain, Auto-GPT, CrewAI, SuperAGI, and OpenAI Function Calling. - Design collaborative multi-agent ecosystems with negotiation, collaboration, and task delegation capabilities. - Integrate long-term and short-term memory (e.g., vector databases, episodic memory) into agents. - Develop, fine-tune, and optimize foundation models (LLMs, diffusion models) using TensorFlow, PyTorch, or JAX. - Apply model compression, quantization, pruning, and distillation techniques for efficient deployment. - Utilize cloud AI services such as AWS SageMaker, Azure ML, Google Vertex AI for scalable model training and serving. - Lead research in Agentic AI, LLM orchestration, and advanced planning strategies (e.g., ReAct, Tree of Thought, Reflexion, Autoformalism). - Stay up-to-date with state-of-the-art research; contribute to whitepapers, blogs, or top-tier conferences (e.g., NeurIPS, ICML, ICLR). - Evaluate new architectures like BDI models, cognitive architectures (e.g., ACT-R, Soar), or neuro-symbolic approaches. - Exhibit strong coding skills in Python, CUDA, and TensorRT for model acceleration. - Experience with distributed computing frameworks (e.g., Ray, Dask, Apache Spark) for training large-scale models. - Design and implement robust MLOps pipelines with Docker, Kubernetes, MLflow, and CI/CD systems. Qualifications Required: - 8-14 years of experience in AI/ML, with at least 2+ years of hands-on experience with Agentic AI systems. - Proven track record in building, scaling, and deploying agent-based architectures. - Strong theoretical foundation in machine learning, deep learning, NLP, and reinforcement learning. - Familiarity with cognitive architectures, decision-making, and planning systems. - Hands-on experience with LLM integration and fine-tuning (e.g., OpenAI GPT-4, Claude, LLaMA, Mistral, Gemini). - Deep understanding of prompt engineering, function/tool calling, retrieval-augmented generation (RAG), and memory management in agentic systems.,

As a Research Scientist - Core AI Lead, your role will involve inventing new methods in deep learning and foundation models. You will be responsible for owning the research cycle from literature review to hypothesis formulation, method design, conducting experiments, writing papers, creating open-source artifacts, and transferring knowledge to product teams. Additionally, you will build rigorous evaluation processes including benchmarks, baselines, statistical tests, and ensuring reproducibility. Collaboration with engineers to scale training and inference by implementing distributed training, profiling, and improving efficiency will also be a key aspect of your role. Key Responsibilities: - Invent new methods in deep learning and foundation models - Own the research cycle end-to-end - Build rigorous evaluation processes - Collaborate with engineers to scale training and inference Qualifications Required: - PhD (preferred) or MS (Research) in AI/ML/CS - First-author, peer-reviewed papers proposing new models/algorithms/objectives in top venues within the last 3-4 years - Demonstrated experience in developing solutions for tabular/structured problems - Strong understanding of math and systems including linear algebra, probability, optimization, and experience with PyTorch or JAX - Exposure to agentic AI systems and experience in research related to single or multi-agent systems Additional Details: The company is not looking for candidates with computer-vision-heavy profiles or roles limited to prompting/RAG/agent wiring without novel methods or first-author papers. They are also not considering candidates with only application/survey papers without code or ablations to reproduce claims. If you meet the requirements and are interested in this position, you can share your resume to sandhia@hiresquad.in.,

As a potential candidate for the position at the top IT product company, you will be responsible for leading the strategic evolution of India's most sophisticated BFSI AI platform ecosystem. This will involve directing a 40-person engineering and data science team to deliver agentic platform capabilities through cutting-edge agentic tech stack. Your role will encompass agent creation and orchestration, AI-native vertical-focused workflows and journeys, along with scalable AI/ML infrastructure. **Key Responsibilities**: - Define and lead AI platform technology strategy, driving innovation across agentic, low-code, and document science platforms, advanced LLM search, and next-gen financial products. - Architect multi-agent, autonomous workflow solutions and ensure scalable, resilient ML infrastructure to support cross-domain product delivery. - Create and own the technology roadmap aligned to strategic business goals and competitive market positioning. - Lead and scale the AI engineering and Data Science team from 40+, building organizational excellence in MLEs, MLOps, and data engineering. - Establish and champion best practices in AI governance, ethical frameworks, and business impact measurement. - Drive cross-functional stakeholder engagement, collaborating closely with product, design, data, and industry partners to accelerate platform innovation and industry leadership. - Represent the company as an authority on AI within industry forums, publications, and speaking events. - Foster a culture of continuous learning, mentorship, and innovation, developing high-potential AI talent for next-generation leadership. - Own and report platform success metrics, business impact KPIs, and deliver on ambitious product growth. **Qualifications & Experience**: - Masters or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or related field from a globally ranked institution. - 12+ years of experience driving large-scale enterprise apps, including 8+ years building enterprise AI platforms and delivering multi-product rollouts in a BFSI/fintech domain. **Skills**: - Hands-on experience scaling AI/ML applications (e.g., Uvicorn, vLLM) in production. - Advanced orchestration of large ML systems and agentic workflows end-to-end. - Evaluation frameworks across classical ML and GenAI (task metrics, robustness, safety). - Deep infrastructure understanding (GPU/CPU architecture, memory/throughput) and MLOps for model operationalization. - Application architecture expertise: modular design, shared large-model services across multiple application components. - Modern cloud proficiency: AWS, GCP (compute, networking, storage, security). - Strong programming discipline and production deployment best practices. - Team scaling & mentoring; effective cross-functional leadership. - Business outcome-driven product strategy and prioritization. Example technical challenges that you may face in this role include designing scalable document AI and agentic search workflows for high-volume BFSI use cases, deploying autonomous ML systems supporting real-time lending and regulatory compliance, and orchestrating and optimizing multi-agent workflows for financial products lifecycle. If you believe you meet the qualifications and experience required for this role, please share your resumes with renu@marketscope.in.,

As an AI Architect specializing in Agentic AI and Generative AI, your role will involve designing, developing, and deploying cutting-edge autonomous AI systems. You will be responsible for building LLM-powered agents with memory, tool usage, planning, and reasoning capabilities to create intelligent, goal-oriented systems. Your technical leadership will oversee AI initiatives from research and architecture design to deployment in cloud environments. Key Responsibilities: - Architect and construct LLM-based agents for autonomous task execution, memory management, tool usage, and multi-step reasoning. - Develop modular, goal-oriented agentic systems utilizing tools like LangChain, Auto-GPT, CrewAI, SuperAGI, and OpenAI Function Calling. - Design collaborative multi-agent ecosystems with negotiation, collaboration, and task delegation capabilities. - Integrate long-term and short-term memory (e.g., vector databases, episodic memory) into agents. - Develop, fine-tune, and optimize foundation models (LLMs, diffusion models) using TensorFlow, PyTorch, or JAX. - Apply model compression, quantization, pruning, and distillation techniques for efficient deployment. - Utilize cloud AI services such as AWS SageMaker, Azure ML, Google Vertex AI for scalable model training and serving. - Lead research in Agentic AI, LLM orchestration, and advanced planning strategies (e.g., ReAct, Tree of Thought, Reflexion, Autoformalism). - Stay up-to-date with state-of-the-art research; contribute to whitepapers, blogs, or top-tier conferences (e.g., NeurIPS, ICML, ICLR). - Evaluate new architectures like BDI models, cognitive architectures (e.g., ACT-R, Soar), or neuro-symbolic approaches. - Exhibit strong coding skills in Python, CUDA, and TensorRT for model acceleration. - Experience with distributed computing frameworks (e.g., Ray, Dask, Apache Spark) for training large-scale models. - Design and implement robust MLOps pipelines with Docker, Kubernetes, MLflow, and CI/CD systems. Qualifications Required: - 8-14 years of experience in AI/ML, with at least 2+ years of hands-on experience with Agentic AI systems. - Proven track record in building, scaling, and deploying agent-based architectures. - Strong theoretical foundation in machine learning, deep learning, NLP, and reinforcement learning. - Familiarity with cognitive architectures, decision-making, and planning systems. - Hands-on experience with LLM integration and fine-tuning (e.g., OpenAI GPT-4, Claude, LLaMA, Mistral, Gemini). - Deep understanding of prompt engineering, function/tool calling, retrieval-augmented generation (RAG), and memory management in agentic systems.,

As an AI Language Model Developer, you will be responsible for developing, fine-tuning, and deploying Large Language Models (LLMs) for various applications such as chatbots, virtual assistants, and enterprise AI solutions. Your key responsibilities will include: - Building and optimizing conversational AI solutions with a minimum of 1 year of experience in chatbot development. - Implementing and experimenting with LLM agent development frameworks like LangChain, LlamaIndex, AutoGen, and LangGraph. - Designing and developing ML/DL-based models to enhance natural language understanding capabilities. - Working on retrieval-augmented generation (RAG) and vector databases such as FAISS, Pinecone, Weaviate, ChromaDB to improve LLM-based applications. - Optimizing and fine-tuning transformer-based models like GPT, LLaMA, Falcon, Mistral, Claude, etc., for domain-specific tasks. - Developing and implementing prompt engineering techniques and fine-tuning strategies to enhance LLM performance. - Working on AI agents, multi-agent systems, and tool-use optimization for real-world business applications. - Developing APIs and pipelines to integrate LLMs into enterprise applications. - Researching and staying up-to-date with the latest advancements in LLM architectures, frameworks, and AI trends. Qualifications and Skills Required: - 1-4 years of experience in Machine Learning (ML), Deep Learning (DL), and NLP-based model development. - Hands-on experience in developing and deploying conversational AI/chatbots is a plus. - Strong proficiency in Python and experience with ML/DL frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers. - Experience with LLM agent development frameworks like LangChain, LlamaIndex, AutoGen, LangGraph. - Knowledge of vector databases (e.g., FAISS, Pinecone, Weaviate, ChromaDB) and embedding models. - Understanding of Prompt Engineering and Fine-tuning LLMs. - Familiarity with cloud services (AWS, GCP, Azure) for deploying LLMs at scale. - Experience in working with APIs, Docker, FastAPI for model deployment. - Strong analytical and problem-solving skills. - Ability to work independently and collaboratively in a fast-paced environment. Good to Have: - Experience with Multi-modal AI models (text-to-image, text-to-video, speech synthesis, etc.). - Knowledge of Knowledge Graphs and Symbolic AI. - Understanding of MLOps and LLMOps for deploying scalable AI solutions. - Experience in automated evaluation of LLMs and bias mitigation techniques. - Research experience or published work in LLMs, NLP, or Generative AI is a plus. (Note: This job description is based on the provided information and is not exhaustive. For more details, please refer to the source: hirist.tech),

In this role as an AI Agent Engineer at an AI-first edtech company, your primary responsibility will be to design, build, and deploy intelligent conversational and autonomous agents that enhance learning experiences. You will work closely with various teams to integrate multi-modal capabilities, align agent behavior with learning outcomes, and continuously enhance agent capabilities. Your contributions will play a vital role in shaping the core AI systems of the company and impacting the learning experiences of millions of students globally. Key Responsibilities: - Design and implement AI-driven conversational agents and autonomous workflows across various learning products. - Build scalable pipelines for interaction, retrieval-augmented generation, and contextual memory. - Integrate text, voice, image, and video capabilities into learning assistants. - Collaborate with cross-functional teams to ensure agent behavior aligns with learning outcomes and user experience. - Develop evaluation frameworks for accuracy, relevance, safety, and personalization. - Fine-tune prompt chains, embeddings, and tool-use logic for optimal performance. - Ensure compliance with ethical AI practices, hallucination reduction, and content moderation. - Continuously research, test, and integrate new LLMs, APIs, and frameworks to enhance agent capabilities. - Contribute to building reusable AI components and internal SDKs for efficient agent development. - Support A/B testing, telemetry integration, and performance analytics for deployed agents. Required Skills & Qualifications: - Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or a related field. - 1-3 years of hands-on experience in building AI applications, chatbots, or agentic systems. - Strong understanding of LLM architectures, embeddings, vector databases, and RAG frameworks. - Proficiency in Python and key AI/ML libraries (LangChain, LlamaIndex, OpenAI, Hugging Face). - Experience with API integration, orchestration (FastAPI/Flask), and database management (MongoDB/Postgres). - Familiarity with prompt engineering, system design for agents, and evaluation metrics. - Excellent problem-solving, debugging, and documentation skills. - Curiosity to explore emerging AI models, frameworks, and autonomous system designs. Tooling Proficiency: - Frameworks: LangChain, LlamaIndex, OpenAI API, Hugging Face Transformers - Programming: Python, TypeScript (nice to have) - Databases: Pinecone, ChromaDB, FAISS, PostgreSQL - APIs & Deployment: FastAPI, Flask, Docker - Version Control: Git/GitHub - Evaluation & Monitoring: Weights & Biases, PromptLayer, LangFuse - Cloud & CI/CD: AWS, GCP, or Azure (preferred); GitHub Actions, Jenkins (nice to have) Bonus Points: - Experience in building educational or learning-focused AI systems. - Understanding of pedagogy, personalization models, and adaptive learning. - Familiarity with voice agents, speech-to-text, and emotion detection APIs. - Knowledge of graph-based memory or multi-agent frameworks. - Experience conducting AI safety, fairness, and reliability evaluations. Joining this company will provide you with a collaborative, research-driven, and mission-focused environment where you can contribute to the development of cutting-edge AI technologies for educational purposes. You will have the opportunity for career growth, competitive compensation, and a flexible work setup to make a meaningful impact on the global learning community.,

As a Senior Data Scientist at our company, you will play a crucial role in developing and implementing machine learning models and algorithms. Your responsibilities will include working closely with project stakeholders to understand requirements, utilizing statistical and machine learning techniques to analyze complex data sets, staying updated with the latest advancements in AI/ML technologies, and collaborating with cross-functional teams to support various AI/ML initiatives. Key Responsibilities: - Develop and implement machine learning models and algorithms. - Work closely with project stakeholders to understand requirements and translate them into deliverables. - Utilize statistical and machine learning techniques to analyze and interpret complex data sets. - Stay updated with the latest advancements in AI/ML technologies and methodologies. - Collaborate with cross-functional teams to support various AI/ML initiatives. Qualifications: - Bachelor's degree in Computer Science, Data Science, or a related field. - Strong understanding of machine learning, deep learning, and Generative AI concepts. Preferred Skills: - Experience in machine learning techniques such as Regression, Classification, Predictive modeling, Clustering, Deep Learning stack using Python. - Experience with cloud infrastructure for AI/ML on AWS (Sagemaker, Quicksight, Athena, Glue). - Expertise in building enterprise-grade, secure data ingestion pipelines for unstructured data (ETL/ELT) including indexing, search, and advance retrieval patterns. - Proficiency in Python, TypeScript, NodeJS, ReactJS (and equivalent) and frameworks (e.g., pandas, NumPy, scikit-learn, SKLearn, OpenCV, SciPy), Glue crawler, ETL. - Experience with data visualization tools (e.g., Matplotlib, Seaborn, Quicksight). - Knowledge of deep learning frameworks (e.g., TensorFlow, Keras, PyTorch). - Experience with version control systems (e.g., Git, CodeCommit). - Strong knowledge and experience in Generative AI/LLM based development. - Strong experience working with key LLM models APIs (e.g. AWS Bedrock, Azure Open AI/OpenAI) and LLM Frameworks (e.g. LangChain, LlamaIndex). - Knowledge of effective text chunking techniques for optimal processing and indexing of large documents or datasets. - Proficiency in generating and working with text embeddings with understanding of embedding spaces and their applications in semantic search and information retrieval. - Experience with RAG concepts and fundamentals (VectorDBs, AWS OpenSearch, semantic search, etc.), Expertise in implementing RAG systems that combine knowledge bases with Generative AI models. - Knowledge of training and fine-tuning Foundation Models (Athropic, Claud, Mistral, etc.), including multimodal inputs and outputs. Good To Have Skills: - Knowledge and Experience in building knowledge graphs in production. - Understanding of multi-agent systems and their applications in complex problem-solving scenarios. Please note that Pentair is an Equal Opportunity Employer, and we value diversity in our workforce as it brings different perspectives and creative ideas that contribute to our continuous improvement.,

As a Senior Data Scientist at our company, you will be joining a dynamic team with a strong focus on AI/ML analytics. With over 8 years of experience, you will play a key role in leveraging data to drive business insights and innovation. **Key Responsibilities:** - Develop and implement machine learning models and algorithms. - Work closely with project stakeholders to understand requirements and translate them into deliverables. - Utilize statistical and machine learning techniques to analyze and interpret complex data sets. - Stay updated with the latest advancements in AI/ML technologies and methodologies. - Collaborate with cross-functional teams to support various AI/ML initiatives. **Qualifications:** - Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a related field. - Strong understanding of machine learning, deep learning, and Generative AI concepts. In addition to the qualifications, you should have experience in machine learning techniques such as Regression, Classification, Predictive modeling, Clustering, Computer vision (yolo), Deep Learning stack, NLP using python. You should also possess strong knowledge and experience in Generative AI/ LLM based development, along with expertise in building enterprise-grade, secure data ingestion pipelines for unstructured data, including indexing, search, and advanced retrieval patterns. It is preferred that you have proficiency in Python, TypeScript, NodeJS, ReactJS (and equivalent) and frameworks (e.g., pandas, NumPy, scikit-learn), Glue crawler, ETL. Experience with data visualization tools (e.g., Matplotlib, Seaborn, Quicksight), deep learning frameworks (e.g., TensorFlow, Keras, PyTorch), and version control systems (e.g., Git, CodeCommit) will be beneficial for this role. **Good to Have Skills:** - Knowledge and Experience in building knowledge graphs in production. - Understanding of multi-agent systems and their applications in complex problem-solving scenarios. As an Equal Opportunity Employer, Pentair values diversity and believes that a diverse workforce contributes different perspectives and creative ideas that enable continuous improvement.,

**Job Description** As an AI Engineer at our company, you will play a crucial role in designing and implementing AI-driven conversational agents and autonomous workflows across our Tutor, Coach, and Buddy products. Your responsibilities will include: - Building scalable pipelines for LLM interaction, retrieval-augmented generation (RAG), and contextual memory. - Integrating multi-modal capabilities (text, voice, image, video) into learning assistants. - Collaborating with product, data, and backend teams to align agent behavior with learning outcomes and UX. - Developing evaluation frameworks for accuracy, relevance, safety, and personalization. - Fine-tuning and optimizing prompt chains, embeddings, and tool-use logic. - Ensuring compliance with ethical AI practices, hallucination reduction, and content moderation. - Continuously researching, testing, and integrating new LLMs, APIs, and frameworks to enhance agent capabilities. - Contributing to building reusable AI components and internal SDKs for faster agent development. - Supporting A/B testing, telemetry integration, and performance analytics for deployed agents. **Qualifications Required** - Bachelors or Masters degree in Computer Science, AI/ML, Data Science, or a related field. - 3+ years of hands-on experience building AI applications, chatbots, or agentic systems. - Strong understanding of LLM architectures, embeddings, vector databases, and RAG frameworks. - Proficiency in Python and key AI/ML libraries (LangChain, LlamaIndex, OpenAI, Hugging Face). - Experience with API integration, orchestration (FastAPI/Flask), and database management (MongoDB/Postgres). - Familiarity with prompt engineering, system design for agents, and evaluation metrics. - Excellent problem-solving, debugging, and documentation skills. In addition to the above responsibilities and qualifications, you may have an advantage if you possess: **Bonus Skills** - Experience in building educational or learning-focused AI systems. - Understanding of pedagogy, personalization models, and adaptive learning. - Familiarity with voice agents, speech-to-text, and emotion detection APIs. - Knowledge of graph-based memory or multi-agent frameworks. - Experience conducting AI safety, fairness, and reliability evaluations. If you join our team, you can expect: - An opportunity to shape the core AI systems of a fast-scaling EdTech company. - A collaborative, research-driven, and mission-focused environment. - Competitive compensation, ownership, and career growth opportunities. - Flexible work setup and a chance to impact millions of learners globally.,

As an AI/ML Engineering Lead at DataAlchemy.ai, you will play a crucial role in guiding the development and deployment of advanced agentic AI solutions for client-facing projects and internal product platforms. Your responsibilities will include: - Leading the design and implementation of advanced agentic AI architectures for enterprise clients and DataAlchemy.ai's core platforms, focusing on planning, memory, retrieval, and autonomous execution modules. - Architecting robust end-to-end AI pipelines for data collection, pre-processing, model development, fine-tuning, and deployment for both client solutions and internal products. - Collaborating with enterprise clients to understand requirements, define solution strategies, and provide technical leadership throughout project lifecycles. - Managing and mentoring a distributed engineering team (ML, data science, MLOps) to ensure high-quality delivery and innovation. - Ensuring technical quality, performance, and scalability for custom client implementations and DataAlchemy.ai's proprietary agentic platforms. - Staying updated with the latest advancements in LLMs, agent orchestration, retrieval-augmented generation, and adaptive reasoning systems to integrate promising techniques proactively. - Contributing to pre-sales activities, solution demos, proof-of-concept implementations, and technical documentation for stakeholders and users. - Driving code quality, automation, and owning CI/CD pipelines, infrastructure, code reviews, and reproducibility. Qualifications required for this role include: - A Bachelors or Masters degree in Computer Science, AI, Machine Learning, or a related field. - 6+ years of hands-on experience in building and deploying AI/ML solutions in applied, consulting, or product environments. - Proven track record in delivering LLM-based systems, multi-agent architectures, and agentic AI applications using Python, PyTorch, TensorFlow, LangChain, or similar frameworks. - Experience in managing distributed engineering teams, including onboarding, mentoring, and agile execution. - Strong communication skills for client engagement, technical presentations, and cross-team collaboration. - Familiarity with retail, logistics, or enterprise analytics is a plus, along with a keen interest in building scalable automation platforms. At DataAlchemy.ai, you can expect: - Technical ownership of enterprise AI consulting projects and the evolution of core agentic platforms. - A flexible, remote-first work structure that allows you to collaborate globally on impactful solutions. - A fast-paced, outcome-driven culture with opportunities for professional growth. - Competitive compensation and outcome-based incentives.,

As a part of Growqr, you will be involved in building a multi-layered ecosystem powered by AI Agents, personalized GPT models, and human-centric innovation. Our vision is to unlock AI's potential for 1 billion people. **Key Responsibilities:** - Build and deploy agentic systems using n8n, Crew.AI, Google Agent-to-Agent protocols, or similar technologies. - Design, orchestrate, and optimize enterprise-grade automation pipelines across ERP, CRM, WMS, and other applications. - Implement scalable integrations using Zapier, Make, or custom APIs for cross-platform workflows. - Work with Gen-AI APIs (OpenAI, Gemini, Anthropic, etc.) to power autonomous decision-making and user-facing experiences. - Apply prompt engineering best practices to reduce cost, increase accuracy, and optimize token usage. - Troubleshoot agent behaviors, debug failures, and ensure high-quality response consistency. - Maintain a strong focus on data quality, application design principles, and process reliability. - Contribute to building internal IP via custom GPTs, LLM orchestration, and vector database-driven workflows. - Take responsibility from concept through production deployment, ensuring scalable outcomes for a global user base. **Required Qualifications:** - 5-7 years of experience in data-driven application development, process automation, or enterprise applications. - Strong understanding of Python programming and ML fundamentals. - Experience integrating or working with ERP, CRM, WMS, or other enterprise systems. - 1-2 years hands-on experience with agentic frameworks (n8n, Crew.AI, Google A2A, AutoGen, etc.). - Solid grounding in application workflow design principles and automation orchestration. - Understanding of token usage, prompt efficiency, and Gen-AI cost management. - Knowledge of vector databases (FAISS, Pinecone, Weaviate, Milvus, etc.) for embeddings-based workflows. - Track record of delivering production-grade agentic automation in fast-paced environments. **Preferred Skills:** - Exposure to MLOps practices and CI/CD pipelines for automation. - Familiarity with multi-agent collaboration and advanced orchestration patterns. - Experience in data privacy, compliance, and secure AI workflows. - Understanding of UX considerations when deploying agentic systems to end-users. - Cloud experience with AWS, GCP, or Azure. As an individual, you are expected to have a builder mindset with a passion for automation and AI agentic ecosystems. You should be proactive, resourceful, able to take minimal direction, and deliver outcomes. Comfortable working across enterprise applications, AI APIs, and automation tools, you should be excited to shape the future of autonomous AI agents in production at scale. As a part of Growqr, you will be involved in building a multi-layered ecosystem powered by AI Agents, personalized GPT models, and human-centric innovation. Our vision is to unlock AI's potential for 1 billion people. **Key Responsibilities:** - Build and deploy agentic systems using n8n, Crew.AI, Google Agent-to-Agent protocols, or similar technologies. - Design, orchestrate, and optimize enterprise-grade automation pipelines across ERP, CRM, WMS, and other applications. - Implement scalable integrations using Zapier, Make, or custom APIs for cross-platform workflows. - Work with Gen-AI APIs (OpenAI, Gemini, Anthropic, etc.) to power autonomous decision-making and user-facing experiences. - Apply prompt engineering best practices to reduce cost, increase accuracy, and optimize token usage. - Troubleshoot agent behaviors, debug failures, and ensure high-quality response consistency. - Maintain a strong focus on data quality, application design principles, and process reliability. - Contribute to building internal IP via custom GPTs, LLM orchestration, and vector database-driven workflows. - Take responsibility from concept through production deployment, ensuring scalable outcomes for a global user base. **Required Qualifications:** - 5-7 years of experience in data-driven application development, process automation, or enterprise applications. - Strong understanding of Python programming and ML fundamentals. - Experience integrating or working with ERP, CRM, WMS, or other enterprise systems. - 1-2 years hands-on experience with agentic frameworks (n8n, Crew.AI, Google A2A, AutoGen, etc.). - Solid grounding in application workflow design principles and automation orchestration. - Understanding of token usage, prompt efficiency, and Gen-AI cost management. - Knowledge of vector databases (FAISS, Pinecone, Weaviate, Milvus, etc.) for embeddings-based workflows. - Track record of delivering production-grade agentic automation in fast-paced environments. **Preferred Skills:** - Exposure to MLOps practices and CI/CD pipelines for automation. - Familiarity with multi-agent collaboration and advanced orchestration patterns. - Experience in data privacy, compliance, and secure AI workflows. - Understanding of UX considerations when

As a part of the team at Growqr in Noida/Delhi NCR, you will be contributing to building a multi-layered ecosystem powered by AI Agents, personalized GPT models, and human-centric innovation. Our vision is to unlock AI's potential for 1 Billion people. **Key Responsibilities:** - Build and deploy agentic systems using n8n, Crew.AI, Google Agent-to-Agent protocols, or similar tools. - Design, orchestrate, and optimize enterprise-grade automation pipelines across ERP, CRM, WMS, and other applications. - Implement scalable integrations using Zapier, Make, or custom APIs for cross-platform workflows. - Work with Gen-AI APIs (OpenAI, Gemini, Anthropic, etc.) to power autonomous decision-making and user-facing experiences. - Apply prompt engineering best practices to reduce cost, increase accuracy, and optimize token usage. - Troubleshoot agent behaviors, debug failures, and ensure high-quality response consistency. - Maintain a strong focus on data quality, application design principles, and process reliability. - Contribute to building internal IP via custom GPTs, LLM orchestration, and vector database-driven workflows. - Take responsibility from concept through production deployment, ensuring scalable outcomes for a global user base. **Required Qualifications:** - 5-7 years of experience in data-driven application development, process automation, or enterprise applications. - Strong Python programming and ML fundamentals understanding. - Experience integrating or working with ERP, CRM, WMS, or other enterprise systems. - 1-2 years hands-on experience with agentic frameworks (n8n, Crew.AI, Google A2A, AutoGen, etc.). - Solid grounding in application workflow design principles and automation orchestration. - Understanding of token usage, prompt efficiency, and Gen-AI cost management. - Knowledge of vector databases (FAISS, Pinecone, Weaviate, Milvus, etc.) for embeddings-based workflows. - Track record of delivering production-grade agentic automation in fast-paced environments. **Preferred Skills:** - Exposure to MLOps practices and CI/CD pipelines for automation. - Familiarity with multi-agent collaboration and advanced orchestration patterns. - Experience in data privacy, compliance, and secure AI workflows. - Understanding of UX considerations when deploying agentic systems to end-users. - Cloud experience with AWS, GCP, or Azure. At Growqr, we are looking for individuals who have a builder mindset with a passion for automation and AI agentic ecosystems. You should be proactive and resourceful, able to take minimal direction and deliver outcomes. It is essential to be comfortable working across enterprise applications, AI APIs, and automation tools. If you are excited to shape the future of autonomous AI agents in production at scale, we would love to have you on board.,

As an AI Engineer, you will be responsible for designing, building, and operating agentic AI systems end-to-end, from concept to production. Your main focus will be on multi-agent orchestration, Retrieval-Augmented Generation (RAG), evaluation frameworks, and AI guardrails to ensure the development of safe, reliable, and high-performing systems. Key Responsibilities: - Design, build, and operate agentic AI systems from concept to production. - Work on multi-agent orchestration and Retrieval-Augmented Generation (RAG) to enhance system performance. - Develop evaluation frameworks and AI guardrails to ensure the safety and reliability of systems. Qualifications Required: - Strong background in AI engineering and machine learning. - Experience in developing AI systems from concept to production. - Ability to collaborate effectively with cross-functional teams including product, ML, and design teams. No additional company details were provided in the job description. As an AI Engineer, you will be responsible for designing, building, and operating agentic AI systems end-to-end, from concept to production. Your main focus will be on multi-agent orchestration, Retrieval-Augmented Generation (RAG), evaluation frameworks, and AI guardrails to ensure the development of safe, reliable, and high-performing systems. Key Responsibilities: - Design, build, and operate agentic AI systems from concept to production. - Work on multi-agent orchestration and Retrieval-Augmented Generation (RAG) to enhance system performance. - Develop evaluation frameworks and AI guardrails to ensure the safety and reliability of systems. Qualifications Required: - Strong background in AI engineering and machine learning. - Experience in developing AI systems from concept to production. - Ability to collaborate effectively with cross-functional teams including product, ML, and design teams. No additional company details were provided in the job description.