Principal Architect AI Data Engineer at EXL Talent Acquisition Team

Gurugram, haryana, India -

Full Time

Start Date

Immediate

Expiry Date

02 Oct, 26

Salary

0.0

Posted On

04 Jul, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Generative AI, Agentic AI, LLM Architecture, RAG Pipelines, LangChain, LangGraph, Python, PySpark, Azure Databricks, Snowflake, MLOps, LLMOps, Prompt Engineering, API Design, Data Engineering, Enterprise Architecture

Industry

Business Consulting and Services

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.)

Responsibilities

Lead the design and implementation of enterprise-grade GenAI and agentic architectures, including RAG pipelines and multi-agent systems. Establish a GenAI Centre of Excellence and drive organizational AI strategy, governance, and stakeholder leadership.