Senior Agentic AI Engineer
First seen by Alion on Sep 19, 2026.
Senior Agentic AI Engineer
Location : Hyderabad
Role Overview :
As an Agentic AI Engineer, you'll design, build, and ship LLM-powered, agentic AI systems that run in production at scale. You won't just prototype you'll own pipelines end-to-end, from prompt design and eval frameworks to distributed microservice deployment and monitoring. You'll take full ownership of everything you build, including end-to-end testing, so that what ships is production-ready, reliable, and safe from day one.
Key Responsibilities :
- Design and build LLM-powered agentic AI pipelines that reason, plan, and execute multi-step workflows with minimal human intervention.
- Own tool and function calling integrations connect LLMs to internal APIs, EHR systems, calendars, and third-party services.
- Build and maintain rigorous eval frameworks : offline eval sets, regression suites, latency benchmarks, and safety/guardrail tests.
- Ship and operate LLMs in production : streaming responses, structured outputs, prompt versioning, cost/latency tracking, and observability.
- Architect and maintain distributed microservices that expose LLM APIs async Python (FastAPI), event-driven pipelines, and real-time WebSocket workflows.
- Build RAG pipelines : chunking strategies, embeddings, vector stores, retrieval tuning, and PHI-safe handling.
- Take full ownership and conduct end-to-end testing for everything you build from unit and integration tests through to production validation and post-deployment monitoring.
- Collaborate closely with product, backend, and QA to define AI requirements and ship reliable, safe features.
Qualifications :
- 3+ years of hands-on Gen AI / LLM engineering experience building and shipping real systems, not just research or prototypes.
- 5+ years of hands-on AI / ML engineering experience overall (Gen AI + pre-Gen AI combined).
- Deep LLM expertise in prompt engineering, tool/function calling, structured outputs, chain-of-thought, and agent orchestration (LangChain, LangGraph, or similar).
- Proven eval culture that you treat evals as a first-class concern not an afterthought.
- Production LLM experience, integrations with OpenAI / Azure OpenAI / AWS Bedrock, streaming, cost optimisation, and monitoring.
- Strong software engineering fundamentals in Python (strong), async programming, REST APIs, CI/CD, Docker.
- Distributed systems fluency with microservice design, event-driven architecture (Kafka/SQS/RabbitMQ), and LLM API gateway patterns.
- End-to-end ownership mindset, you write tests, validate in staging, and don't consider a feature done until it's verified in production.
Preferred Skills :
- LLM post-training experience with fine-tuning, RLHF, DPO, or LoRA for domain-adapted or instruction-tuned models.
- Automatic prompt optimisation familiarity with tools or techniques like DSPy, TextGrad, or automated prompt search for systematic prompt improvement.
- Healthcare industry experience, working knowledge of EHR systems, HL7/FHIR standards, HIPAA compliance, or clinical workflows.
- Startup experience where you've worked in a fast-moving, resource constrained environment where you wore multiple hats and shipped quickly.
- Scaling and on-prem deployments experience deploying LLMs at scale, including self-hosted / on-prem model serving (vLLM, TGI, Triton) or hybrid cloud architectures.
Skills
Python, Machine Learning, Generative AI, LangChain, FastAPI, LangGraph, Agentic AI, Artificial Intelligence, LLM, OpenAI, LORA