Description
Key Responsibilities
1. Solution Architecture & Strategy
- Define and lead end-to-end architecture for enterprise GenAI platforms and use cases
- Design scalable agentic systems (single-agent, multi-agent, orchestration frameworks)
- Establish reference architectures, design patterns, and reusable frameworks
- Lead architecture decisions on RAG vs fine-tuning vs hybrid approaches
- Conduct technology evaluations (LLMs, vector DBs, orchestration frameworks) and recommend best-fit solutions
2. Agentic AI & LLM Engineering Leadership
- Design and implement complex agentic workflows with tool calling, function orchestration, and memory strategies
- Build enterprise-grade RAG pipelines with robust focus on retrieval accuracy and evaluation
- Drive prompt architecture standards (prompt libraries, chaining, orchestration governance)
- Optimise solutions for latency, cost, scalability, and reliability
3. Platform & Engineering Excellence
- Lead development of GenAI platforms, APIs, and microservices (FastAPI, Flask, etc.)
- Define engineering best practices:
coding standards, testing, packaging, observability
- Ensure seamless integration with enterprise data platforms, APIs, and business applications
- Collaborate with MLOps teams for CI/CD, deployment pipelines, versioning, and monitoring
4. Governance, Risk & Responsible AI
- Define and enforce LLM guardrails (hallucination control, safety filters, policy enforcement)
- Implement evaluation frameworks (RAG evaluation, prompt testing, benchmarking)
- Ensure compliance with data security, privacy, and enterprise governance standards
- Drive adoption of Responsible AI practices (bias mitigation, explainability, auditability)
5. Data & Ecosystem Collaboration
- Partner with Data Engineering teams on:
- Data ingestion, pipelines, and quality controls
- Metadata management and knowledge graph strategies
- Work with business stakeholders to:
- Identify high-value GenAI use cases
- Translate business problems in
📌 Architect AI Data Engineer (Pune)
🏢 EXL
📍 Pune