Senior AI / RAG Engineer Applied LLM & Retrieval (Telangana)

Senior AI / RAG Engineer Applied LLM & Retrieval This is a deeply hands-on role for an experienced engineer who wants to spend their time building. A big part of the job is growing our fleet of AI sub-agents designing new specialized agents and wiring them into complete, end-to-end multi-agent agentic workflows and architecture. You'll design, implement, and harden our RAG, agent, and LLM features end-to-end the retrieval pipelines, the agent orchestration, the model-gateway routing and fallback logic, the AI automation pipelines, and the evaluations that keep quality high all inside a GDPR-first, EU-only data boundary. Your work ships to production and you own it through to it working reliably. What you'll do - Grow our fleet of AI sub-agents design and build more and more specialized agents, and wire each into a complete multi-agent, agentic AI-driven workflow and architecture, from single-purpose agents up to orchestrated end-to-end flows. - Build and improve RAG pipelines chunking, embeddings, vector storage and retrieval, re-ranking, grounding and citation over proprietary data such as work orders, rate cards, and uploaded documents. - Build LLM features on cloud-hosted large language models classification, extraction, summarisation, structured/JSON output, prioritisation, and multimodal (text + image) reasoning from prototype through to production. - Extend the internal Model Gateway task/tier model routing, retries, fallback, cost estimation, and per-tenant usage limits. - Build adversarial validation agents AI agents that critique and stress-test model outputs, plans, and designs over multiple rounds to catch errors and edge cases before they ship. - Design and build AI automation pipelines orchestrated, repeatable AI workflows, both in the product and across our engineering process. - Automate the SDLC with AI bring AI into how we plan, generate, review, test, and ship code, and build the tooling that makes it repeatable. - Integrate and extend an automated testing framework including AI-assisted test generation and self-checking pipelines for our AI features. - Build the evaluations datasets and harnesses that measure accuracy, faithfulness/hallucination, latency, and cost, and catch regressions in non-deterministic systems before release. - Do the prompt engineering schema-validated outputs, tool/function calling, and the tuning needed to reduce hallucination and hit quality targets. - Enforce data-protection by design tenant-scoped retrieval, PII handling, and erasure/cascade in the retrieval layer; keep every data flow inside the EU/EEA. - Ship to production on AWS (Python, ECS) with proper observability, and own your features through to reliable operation. Required skills & experience - Solid experience in software engineering, with deep, production-grade Python. - Substantial hands-on experience building and shipping LLM-powered systems to production you've taken more than one from idea to reliable, scaled feature and stayed close to the code. - Designing and composing multi-agent systems building specialized sub-agents and wiring them into complete, end-to-end agentic workflows and architecture. - Deep RAG expertise embeddings, vector stores (e.g. pgvector, OpenSearch, Pinecone, FAISS), semantic search, chunking strategies, re-ranking, grounding and the judgement to know what actually moves quality. - Embeddings at scale and multilingual retrieval. - Advanced prompt engineering and structured output / tool use / function calling with schema-validated (JSON) responses; strong instincts for reducing hallucination. - Deep experience with cloud-hosted commercial LLM APIs / a managed LLM platform. - Agentic / multi-agent frameworks (LangGraph, LangChain, or similar) orchestration and state machines including building adversarial / validation agents or LLM-as-judge setups. - AI-driven SDLC automation and experience building AI automation pipelines (CI/CD or workflow orchestration). - Integrating automated testing frameworks and AI-assisted test automation. - Proven ability to build evaluations for AI systems datasets, metrics (accuracy, faithfulness, latency, cost), and regression testing. - Real command of cost/latency trade-offs model tiering, routing, fallback, and caching at production scale. - Strong AWS background building, deploying, and operating services in production; IAM and region-aware design. - Streaming, latency optimisation, and prompt caching. - Vector-DB operations, observability (Datadog / CloudWatch), and IaC. - GDPR / data-residency / responsible-AI experience no-training commitments, tenant isolation, DPAs, PII minimisation. - Experience with AI systems running at production scale. - Excellent testing discipline, code quality, and engineering judgement; comfortable owning a feature end-to-end. Senior AI / RAG Engineer Applied LLM & Retrieval This is a deeply hands-on role for an experienced engineer who wants to