01Responsibilities
Key Responsibilities
GenAI & Agentic AI Architecture
- Define enterprise reference architectures for Agentic AI and LLM-powered platforms , including:
- Single-agent and multi-agent systems
- Tool-calling and function orchestration
- Memory, planning, and execution layers
- Own architectural decisions for Claude / Claude Code and other enterprise-grade LLMs , including model selection, deployment patterns, and costlatency trade-offs.
- Design secure-by-default GenAI systems incorporating:
- Guardrails and policy enforcement
- Data privacy, PII handling, and prompt safety
- Controlled tool execution in regulated environments
RAG, Knowledge & Data Systems
- Architect large-scale RAG solutions , covering:
- Data ingestion and curation pipelines
- Chunking and embedding strategies
- Vector databases and hybrid search
- Evaluation and feedback loops
- Partner with Data Engineering teams to ensure data quality, lineage, observability, and governance for AI-driven systems.
Platform & Engineering Excellence
- Drive production readiness of GenAI systems:
- API-first design (FastAPI / REST / event-driven)
- CI/CD for LLM workflows
- Monitoring, evaluation, and cost tracking
- Establish engineering standards, reusable frameworks, and accelerators for faster adoption across EXL accounts.
- Review and influence cloud architecture (Azure / AWS / GCP) for scalable and compliant AI deployments.
Leadership & Stakeholder Engagement
- Act as a technical .
