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.