Gen AI Developer/AI ML Engineer

Job Description:

Responsibilities

• Build LLM/RAG services in Python (FastAPI/asyncio, Pydantic) with clean APIs and tests.
• Implement agentic AI workflows — tool-using agents with planning, memory, multi-step execution, and recovery/fallback paths.
• Stream responses server-side from model to UI (SSE/WebSockets with retries, backoff, partial responses, and cancellation).
• Build RAG pipelines: ingestion, chunking, embeddings, indexing, reranking, and grounded answers with citations (pgvector/FAISS/Pinecone).
• Manage prompt versioning, templates/params, safe fallbacks, and rollbacks.
• Run evaluations: hallucination/groundedness checks, regression suites for prompts and retrievers.
• Implement guardrails: PII detection/redaction, content safety, and domain constraints.
• Optimise for performance and cost: context reduction, caching/batching, request pacing, and rate-limit handling.
• Design and ship REST/gRPC endpoints that orchestrate tools, retrieval, and post-processing (citations/formatting).
• Implement and consume MCP (Model Context Protocol) tool adapters — files, web, DB connectors — with capability negotiation, auth/permissions, and resource limits.
• Integrate vector stores (pgvector/FAISS/Pinecone) plus DB/file stores and enterprise connectors (SharePoint, Confluence, Slack, Jira).
• Own AuthN/Z and tenancy: JWT/OAuth, role/tenant isolation, secrets management, and audit logging for AI actions.
• Set up observability: logs, metrics, traces, and dashboards/alerts for latency, error rate, and token spend.
• Manage queues/workflows for background ingestion/summarisation with idempotency and retries.
• Enforce data governance: input/output validation, schema contracts, PII handling, and retention policies.
• Maintain CI/CD for prompts, retrieval configs, and API changes; support blue/green or canary releases.

Qualification & Experience 

• Strong Python skills — FastAPI, asyncio, Pydantic; clean, tested, production-ready code.
• Hands-on experience building LLM-powered applications (OpenAI, Gemini, Llama, or similar).
• Experience with RAG architectures and vector databases (pgvector, FAISS, Pinecone, or similar).
• Familiarity with agentic frameworks (LangChain, LlamaIndex, CrewAI, or custom implementations).
• Knowledge of streaming APIs: SSE and WebSockets.
• Understanding of prompt engineering, prompt versioning, and evaluation techniques.
• Experience integrating REST/gRPC APIs and enterprise connectors.
• Awareness of AI safety, guardrails, PII handling, and responsible AI practices.
• Familiarity with observability tooling (OpenTelemetry, Grafana, Prometheus, or similar).
• Good understanding of CI/CD pipelines and DevOps practices in an AI/ML context.