AI Data Engineer Lead

Job Description

Key Responsibilities

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 & Must-Have Skills

Experience

• 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)

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.)