Job Description: 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 .
Responsibilities: 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 .
Qualifications: 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 .
