Principal Architect AI Data Engineer

EXL; Gurugram District; On-site; INR 4,000,000 - 9,000,000; Full time

Job summary

EXL is seeking an experienced GenAI Architecture & Solution Leader to drive enterprise-grade GenAI and agentic architectures at scale in the Indian market. You will shape reference architectures, reusable frameworks, and best practices for LLM applications across the organisation.

You will oversee end-to-end RAG pipelines, mentor teams on prompt engineering and multi-agent workflows, and collaborate with data engineering for data quality, governance and compliant enterprise integration.

Qualifications

• 15+ years of experience in Data Engineering / Data Science / AI
• 3+ years hands-on experience in LLM / GenAI solutions at scale
• Proven architecture, solution design, and enterprise delivery expertise

Responsibilities

• Lead the design of enterprise-grade GenAI and agentic architectures across the organisation.
• Define reference architectures, reusable frameworks, and best practices for LLM applications.
• Architect and oversee end-to-end RAG pipelines: Data ingestion → chunking → embeddings → vector search → orchestration → response synthesis.
• Provide technical leadership in prompt engineering, prompt orchestration, and agent workflows (LangChain, LangGraph).
• Drive scalability, reliability, cost optimisation, and performance across GenAI platforms.
• Establish enterprise governance frameworks for GenAI, responsible AI, security, privacy, and compliance.

Skills

Python

PySpark

API integration

LangChain

LangGraph

RAG pipelines

LLMs

Agentic AI

Prompt engineering

MLOps

CI/CD

Monitoring

Azure Databricks

Snowflake

Cloud platforms

NLP

Data Engineering

Data Science

SQL

Containers

Tools

LangChain

LangGraph

FastAPI

Flask

APIs

Job description

Key Responsibilities

• Lead the design of enterprise-grade GenAI and agentic architectures (single-agent, multi-agent, tool-driven systems).
• Define reference architectures, reusable frameworks, and best practices for LLM applications across the organisation.
• Drive scalability, reliability, cost optimisation, and performance across GenAI platforms.

Architecture & Solution Leadership

• Lead the design of enterprise-grade GenAI and agentic architectures (single-agent, multi-agent, tool-driven systems).
• Define reference architectures, reusable frameworks, and best practices for LLM applications across the organisation.
• Architect and oversee implementation of end-to-end RAG pipelines: - Data ingestion → chunking → embeddings → vector search → orchestration → response synthesis.
• Drive scalability, reliability, cost optimisation, and performance across GenAI platforms.

Agentic & LLM Engineering (Hands-on + Oversight)

• Provide technical leadership in prompt engineering, prompt orchestration, and agent workflows (LangChain, LangGraph, etc.).
• Guide teams on tool-calling, function-calling, memory handling, and multi-agent system design.
• Lead efforts in hallucination reduction, guardrails, safety mechanisms, and output evaluation frameworks.

Platform & Engineering Excellence

• Architect production-grade APIs and services (FastAPI/Flask/enterprise microservices) for LLM solutions.
• Define MLOps / LLMOps pipelines including CI/CD, monitoring, observability, and evaluation.
• Partner with Data Engineering teams to ensure: - Data quality, lineage, governance, and compliance
• Seamless integration with enterprise data platforms

Organisation-Level Responsibilities (Critical)

Capability Building & CoE Development

• Build and scale GenAI / Agentic AI Centre of Excellence (CoE).
• Define standardised frameworks, accelerators, and reusable components to improve delivery velocity.
• Drive organisation-wide adoption of GenAI best practices and tooling standards.

Strategic & Stakeholder Leadership

• Engage with CXOs, business stakeholders, and clients to translate business problems into AI-led solutions.
• Lead solutioning, pre-sales, RFP responses, and client workshops for GenAI opportunities.
• Influence AI strategy, roadmap, and investment decisions at organisational level.

Governance, Risk & Compliance

• Establish enterprise governance frameworks for GenAI: - Responsible AI, security, privacy, ethical usage, and compliance
• Define policies for: - Data access, redaction, model usage, auditability, and explainability

Mentorship & Team Leadership

• Mentor and guide architects, engineers, and data scientists.
• Drive technical upskilling, hiring strategy, and capability maturity.
• Review solution designs and enforce architecture quality standards.

Experience

Experience & Must-Have Skills

• 15+ years of total experience in Data Engineering / Data Science / AI
• 3+ years of hands-on experience in LLM / GenAI solutions at scale
• Proven experience in architecture, solution design, and enterprise delivery

LLM / GenAI & Agentic Engineering

• Strong hands-on experience with: - LLMs (Claude, OpenAI, etc.)
• RAG pipelines and retrieval optimisation
• GPT + Agentic AI implementation experience
• Experience with: - LangChain, LangGraph, or similar frameworks
• Agent orchestration and tool-calling architectures
• Deep understanding of: - LLM limitations, evaluation, and optimisation strategies

Core Engineering

• Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience
• Deep data analysis experience and handling large volume of data
• Fabric/Azure Databricks/Snowflake data engineering integration skills
• Good exposure to: - Cloud platforms (Azure/AWS/GCP)
• SQL
• Containers, CI/CD, monitoring

Data / AI Foundations (Mandatory)

• Data Engineering (ETL/ELT, pipelines, orchestration)
• Data Science / ML lifecycle (especially NLP)

Prior Experience In One Or More

• Data Engineering (ETL/ELT, pipelines, orchestration)
• Data Science / ML lifecycle (especially NLP)

Analytics engineering / data products

Good-to-Have / Preferred

• Fine-tuning techniques (LoRA, PEFT, prompt tuning, few-shot learning)
• Experience with enterprise GenAI deployments (security, privacy, governance)
• Experience with Azure ecosystem (Azure OpenAI, AI Search, Fabric, etc.)
• Exposure to industry use cases (Insurance, BFSI, Healthcare, Retail, etc.)