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JD - Software Engineer

The job description outlines a role requiring proficiency in Python and experience with machine learning, deep learning, and generative AI models. Responsibilities include collaborating on ML projects, optimizing AI models, and developing NLP solutions while ensuring software design and usability. The candidate should have experience with various databases, cloud services, and software engineering best practices.

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008 Aditya Srivastava
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18 views1 page

JD - Software Engineer

The job description outlines a role requiring proficiency in Python and experience with machine learning, deep learning, and generative AI models. Responsibilities include collaborating on ML projects, optimizing AI models, and developing NLP solutions while ensuring software design and usability. The candidate should have experience with various databases, cloud services, and software engineering best practices.

Uploaded by

008 Aditya Srivastava
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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Job description

Technical Skillset:

 Proficient in Python, with experience in machine learning, deep learning, CV and NLP processing.
 Experience in developing and implementing generative AI models, with a strong understanding of
deep learning techniques such as GPT, VAE, and GANs.
 Prompt Engineering: The engineer prompts and optimizes few-shot techniques to enhance LLM's
performance on specific tasks, e.g. personalized recommendations.
 Model Evaluation & Optimization: Evaluate LLM's zero-shot and few-shot capabilities, fine-tuning
hyperparameters, ensuring task generalization, and exploring model interpretability for robust
web app integration.
 Response Quality: Collaborate with ML and Integration engineers to leverage LLM's pre-trained
potential, delivering contextually appropriate responses in a user-friendly web app.
 Databases: Experience with vector databases, RDBMS, MongoDB and NoSQL databases.
 Hands-on experience with Restful services, Python, Hugging Face, TensorFlow, Keras, PyTorch
 Experience as data modeling ML/NLP scientist. including, but not limited to, Performance tuning,
fine-tuning, RLHF, and performance optimization. Validated background with ML toolkits, such
as PyTorch, TensorFlow, Keras, Langchain, Llamadindex
. Cloud: Prior experience in developing AWS cloud services. Knowledge of GCP / Azure is desirable.

Role and responsibilities -


 Collaborating with cross-functional teams to develop end to end ML, NLP and Gen AI based
projects in AWS environment
 Conducting research to stay up to date with the latest advancements in generative AI machine
learning and deep learning techniques
 Optimizing existing generative AI models for improved performance scalability and efficiency and
building reusable modules.
 Evaluating and selecting appropriate AI tools and machine learning models for tasks, as well as
building and training working versions of those models using Python and other open-source
technologies
 Hands-on experience leveraging large sets of structured and unstructured data to develop data-
driven tactical and strategic analytics and insights using ML, NLP, computer vision solutions
 Strong sense of software design and usability of ML systems
 Experience applying software engineering methodologies and best practices including coding
standards, code reviews, build processes, testing, and security.
 Develop natural language processing (NLP) solutions using GenAI, LLMs and custom transformer
architectures.

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