I am an enterprise AI engineering, product, and solutions executive with 25+ years of experience building technology platforms, leading global teams, and translating complex operational problems into products that customers can adopt and organizations can scale.

My work sits at the intersection of engineering, product, consulting, and commercial impact. I remain hands-on with AI architecture and rapid prototyping while also shaping the customer problem, operating model, implementation roadmap, governance, and value case.

I am the founder of Proxiom.ai, focused on enterprise reasoning systems, root-cause analysis, knowledge orchestration, workflow automation, and governed AI deployment.

flowchart LR
    A[Business decision or workflow] --> B[Context and permissions]
    B --> C[Structured data]
    B --> D[Documents and knowledge]
    B --> E[Events, logs and history]
    C --> F[Reasoning workflow]
    D --> F
    E --> F
    F --> G[Models and specialized agents]
    F --> H[Rules, tools and enterprise APIs]
    G --> I[Evidence-backed recommendation or action]
    H --> I
    I --> J{Human decision required?}
    J -->|Yes| K[Review, approve, correct or escalate]
    J -->|No, within policy| L[Governed execution]
    K --> M[Outcome, audit and learning]
    L --> M
    M --> F

The foundation model is an important component, but rarely the durable enterprise moat. Production value comes from the accumulated system around the model: proprietary context, workflow history, permissions, integrations, evaluation, operating controls, and organizational memory.

• Models and orchestration: model routing, structured generation, tool use, RAG, multi-agent workflows, LangGraph-style state machines, memory, and human escalation
• Data and knowledge: relational data, event streams, vector retrieval, knowledge graphs, semantic layers, quality, lineage, and permission-aware access
• Platform engineering: APIs, containers, Kubernetes, CI/CD, model lifecycle, prompt and workflow versioning, observability, evaluation, and cost controls
• Enterprise integration: Salesforce, NetSuite, ServiceNow, Jira, enterprise content, cloud services, data platforms, and operational systems
• Regulated workflows: privacy, auditability, explainability, access controls, policy enforcement, human accountability, and production risk management

• Built and led a global organization of more than 100 professionals across AI engineering, data science, product management, business intelligence, managed services, and technology operations
• Connected data, analytics, AI, applications, and customer operations around shared commercial and operational outcomes for a business with more than $3 billion in revenue
• Built AI-enabled products and workflows across legal operations, sales and marketing, customer operations, enterprise knowledge, incident management, and managed services
• Led at the intersection of product strategy, architecture, customer delivery, adoption, organizational change, and executive stakeholder management
• Earlier career includes a decade of B2B sales and consulting experience across the United States, India, and Japan

The same architecture principles apply across healthcare, financial services, legal operations, technology operations, and commercial workflows. The domain changes, but the core design problem remains consistent:

Combine trusted evidence, domain policy, enterprise data, specialized tools, model reasoning, and human judgment around a consequential business decision.

• Product and solution portfolio, deeper case studies across agentic delivery, forward-deployed engineering, incident intelligence, healthcare, legal operations, and GTM AI
• Enterprise AI systems playbook, my approach to discovery, architecture, evaluation, governance, pilots, productionization, and value realization
• Options income and portfolio risk system, multi-broker portfolio intelligence, risk-constrained strategy generation, and guarded trade execution
• Proxiom.ai, enterprise AI reasoning and operations platform work

Discover the decision and measurable outcome
        ↓
Build a narrow, credible prototype
        ↓
Validate with users and domain experts using FDE-Toolkit
        ↓
Establish evaluation, security and human controls
        ↓
Pilot inside the real workflow
        ↓
Productize, operate, measure and expand

I am interested in leadership and advisory opportunities where AI engineering, product strategy, forward-deployed delivery, and customer value come together.

• LinkedIn: linkedin.com/in/amit-vik
• Email: amitvik@gmail.com
• Location: Princeton, New Jersey, United States

_{Public repositories and demonstrations are intentionally designed to avoid customer data, protected information, confidential employer assets, production credentials, and proprietary implementation details.}

A comprehensive public-safe collection of recent enterprise AI and machine-learning work is available in Recent AI and ML repository blueprints.