Principal Architect AI Data Engineer
About the Role
We are seeking a Senior Architect – Agentic / Generative AI with 15+ years of experience to lead the design, architecture, and enterprise-scale implementation of LLM-powered and agentic solutions. This role goes beyond individual contribution and requires end-to-end ownership of GenAI architecture, reusable frameworks, and enterprise adoption strategy. You will define agentic AI blueprints, RAG architectures, governance models, and scalable platforms, while mentoring teams and driving organisation-wide GenAI capabilities. Mandatory: Strong prior experience in Data Engineering or Data Science ecosystems, with deep understanding of data pipelines, ML lifecycle, and enterprise data architecture.
Key Skills for This Role
Full Job Posting
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.)
Preferred Qualifications
- Good-to-Have / Preferred
About EXL
EXL is a global data and AI company that provides analytics, digital operations, and industry-specific services to enterprises in insurance, healthcare, banking, retail, media, and energy.
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