Agentic/AI lead/architect with Claude/code/LLM skills1 at

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About The Role

Key Responsibilities

GenAI & Agentic AI Architecture

Define enterprise reference architectures for Agentic AI and LLM-powered platforms , including:
Single-agent and multi-agent systems
Tool-calling and function orchestration
Memory, planning, and execution layers
Own architectural decisions for Claude / Claude Code and other enterprise-grade LLMs , including model selection, deployment patterns, and cost–latency trade-offs.
Design secure-by-default GenAI systems incorporating:
Guardrails and policy enforcement
Data privacy, PII handling, and prompt safety
Controlled tool execution in regulated environments

RAG, Knowledge & Data Systems

Architect large-scale RAG solutions , covering:
Data ingestion and curation pipelines
Chunking and embedding strategies
Vector databases and hybrid search
Evaluation and feedback loops
Partner with Data Engineering teams to ensure data quality, lineage, observability, and governance for AI-driven systems.

Platform & Engineering Excellence

Drive production readiness of GenAI systems:
API-first design (FastAPI / REST / event-driven)
CI/CD for LLM workflows
Monitoring, evaluation, and cost tracking
Establish engineering standards, reusable frameworks, and accelerators for faster adoption across EXL accounts.
Review and influence cloud architecture (Azure / AWS / GCP) for scalable and compliant AI deployments.

Leadership & Stakeholder Engagement

Act as a technical authority for GenAI across delivery teams and client engagements.
Mentor senior engineers, tech leads, and architects on agentic patterns and advanced LLM engineering.
Partner with clients, product owners, and domain SMEs to shape AI roadmaps, solution designs, and value articulation .

Mandatory Skills & Experience

12+ years of total experience with deep hands-on expertise in Generative AI / LLM-based systems , and strong prior background in Data Engineering or Data Science (mandatory) .

Generative AI / LLM Expertise

Deep hands-on experience with:
Claude / Anthropic ecosystem (including Claude Code exposure is a strong plus)
Other enterprise LLMs (OpenAI, Mistral, LLaMA, etc.)
Strong command over:
Prompt engineering, prompt orchestration, and agent workflows
Tool/function calling, planning–execution loops
LLM and RAG evaluation techniques (precision, grounding, faithfulness)

Agentic & RAG Architecture

Proven experience designing:
Agentic AI systems (ReAct, Plan-and-Execute, multi-agent setups)
RAG architectures using vector databases (FAISS, Pinecone, Chroma, etc.)
Strong understanding of hallucination mitigation, guardrails, and safety frameworks .

Core Engineering & Platform Skills

Expert-level Python engineering (production-grade systems).
Strong experience with cloud-native AI solutions on Azure, AWS, or GCP.
API design, microservices, and event-driven architectures.

Mandatory Prior Background

Data Engineering or Data Science experience is non-negotiable , including:
Data pipelines / ETL / ELT / orchestration
ML or NLP model lifecycle
Analytics platforms or data product engineering

Good-to-Have / Preferred

Fine-tuning and adaptation strategies (LoRA / PEFT / prompt tuning).
Experience with MLOps / LLMOps platforms and observability stacks.
Experience delivering GenAI solutions in regulated industries (Insurance, Healthcare, BFS).
Exposure to enterprise AI governance frameworks .

Key Responsibilities

GenAI & Agentic AI Architecture

Define enterprise reference architectures for Agentic AI and LLM-powered platforms , including:
Single-agent and multi-agent systems
Tool-calling and function orchestration
Memory, planning, and execution layers
Own architectural decisions for Claude / Claude Code and other enterprise-grade LLMs , including model selection, deployment patterns, and cost–latency trade-offs.
Design secure-by-default GenAI systems incorporating:
Guardrails and policy enforcement
Data privacy, PII handling, and prompt safety
Controlled tool execution in regulated environments

RAG, Knowledge & Data Systems

Architect large-scale RAG solutions , covering:
Data ingestion and curation pipelines
Chunking and embedding strategies
Vector databases and hybrid search
Evaluation and feedback loops
Partner with Data Engineering teams to ensure data quality, lineage, observability, and governance for AI-driven systems.

Platform & Engineering Excellence

Drive production readiness of GenAI systems:
API-first design (FastAPI / REST / event-driven)
CI/CD for LLM workflows
Monitoring, evaluation, and cost tracking
Establish engineering standards, reusable frameworks, and accelerators for faster adoption across EXL accounts.
Review and influence cloud architecture (Azure / AWS / GCP) for scalable and compliant AI deployments.

Leadership & Stakeholder Engagement

Act as a technical authority for GenAI across delivery teams and client engagements.
Mentor senior engineers, tech leads, and architects on agentic patterns and advanced LLM engineering.
Partner with clients, product owners, and domain SMEs to shape AI roadmaps, solution designs, and value articulation .

Mandatory Skills & Experience

12+ years of total experience with deep hands-on expertise in Generative AI / LLM-based systems , and strong prior background in Data Engineering or Data Science (mandatory) .

Generative AI / LLM Expertise

Deep hands-on experience with:
Claude / Anthropic ecosystem (including Claude Code exposure is a strong plus)
Other enterprise LLMs (OpenAI, Mistral, LLaMA, etc.)
Strong command over:
Prompt engineering, prompt orchestration, and agent workflows
Tool/function calling, planning–execution loops
LLM and RAG evaluation techniques (precision, grounding, faithfulness)

Agentic & RAG Architecture

Proven experience designing:
Agentic AI systems (ReAct, Plan-and-Execute, multi-agent setups)
RAG architectures using vector databases (FAISS, Pinecone, Chroma, etc.)
Strong understanding of hallucination mitigation, guardrails, and safety frameworks .

Core Engineering & Platform Skills

Expert-level Python engineering (production-grade systems).
Strong experience with cloud-native AI solutions on Azure, AWS, or GCP.
API design, microservices, and event-driven architectures.

Mandatory Prior Background

Data Engineering or Data Science experience is non-negotiable , including:
Data pipelines / ETL / ELT / orchestration
ML or NLP model lifecycle
Analytics platforms or data product engineering

Good-to-Have / Preferred

Fine-tuning and adaptation strategies (LoRA / PEFT / prompt tuning).
Experience with MLOps / LLMOps platforms and observability stacks.
Experience delivering GenAI solutions in regulated industries (Insurance, Healthcare, BFS).
Exposure to enterprise AI governance frameworks .

Key Responsibilities

GenAI & Agentic AI Architecture

Define enterprise reference architectures for Agentic AI and LLM-powered platforms , including:
Single-agent and multi-agent systems
Tool-calling and function orchestration
Memory, planning, and execution layers
Own architectural decisions for Claude / Claude Code and other enterprise-grade LLMs , including model selection, deployment patterns, and cost–latency trade-offs.
Design secure-by-default GenAI systems incorporating:
Guardrails and policy enforcement
Data privacy, PII handling, and prompt safety
Controlled tool execution in regulated environments

RAG, Knowledge & Data Systems

Architect large-scale RAG solutions , covering:
Data ingestion and curation pipelines
Chunking and embedding strategies
Vector databases and hybrid search
Evaluation and feedback loops
Partner with Data Engineering teams to ensure data quality, lineage, observability, and governance for AI-driven systems.

Platform & Engineering Excellence

Drive production readiness of GenAI systems:
API-first design (FastAPI / REST / event-driven)
CI/CD for LLM workflows
Monitoring, evaluation, and cost tracking
Establish engineering standards, reusable frameworks, and accelerators for faster adoption across EXL accounts.
Review and influence cloud architecture (Azure / AWS / GCP) for scalable and compliant AI deployments.

Leadership & Stakeholder Engagement

Act as a technical authority for GenAI across delivery teams and client engagements.
Mentor senior engineers, tech leads, and architects on agentic patterns and advanced LLM engineering.
Partner with clients, product owners, and domain SMEs to shape AI roadmaps, solution designs, and value articulation .

Mandatory Skills & Experience

12+ years of total experience with deep hands-on expertise in Generative AI / LLM-based systems , and strong prior background in Data Engineering or Data Science (mandatory) .

Generative AI / LLM Expertise

Deep hands-on experience with:
Claude / Anthropic ecosystem (including Claude Code exposure is a strong plus)
Other enterprise LLMs (OpenAI, Mistral, LLaMA, etc.)
Strong command over:
Prompt engineering, prompt orchestration, and agent workflows
Tool/function calling, planning–execution loops
LLM and RAG evaluation techniques (precision, grounding, faithfulness)

Agentic & RAG Architecture

Proven experience designing:
Agentic AI systems (ReAct, Plan-and-Execute, multi-agent setups)
RAG architectures using vector databases (FAISS, Pinecone, Chroma, etc.)
Strong understanding of hallucination mitigation, guardrails, and safety frameworks .

Core Engineering & Platform Skills

Expert-level Python engineering (production-grade systems).
Strong experience with cloud-native AI solutions on Azure, AWS, or GCP.
API design, microservices, and event-driven architectures.

Mandatory Prior Background

Data Engineering or Data Science experience is non-negotiable , including:
Data pipelines / ETL / ELT / orchestration
ML or NLP model lifecycle
Analytics platforms or data product engineering

Good-to-Have / Preferred

Fine-tuning and adaptation strategies (LoRA / PEFT / prompt tuning).
Experience with MLOps / LLMOps platforms and observability stacks.
Experience delivering GenAI solutions in regulated industries (Insurance, Healthcare, BFS).
Exposure to enterprise AI governance frameworks .