Job DescriptionKey ResponsibilitiesArchitecture & 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 platformsOrganisation-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 explainabilityMentorship & 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.ExperienceExperience & 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 deliveryLLM / 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 strategiesCore Engineering- Solid 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, monitoringData / AI Foundations (Mandatory)Prior Experience In One Or More- Data Engineering (ETL/ELT, pipelines, orchestration)- Data Science / ML lifecycle (especially NLP)Analytics engineering / data productsGood-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.)

📌 Principal Architect Ai Data Engineer (Pune)
🏢 EXL
📍 Pune