{"id":1227625,"url":"https://alion.io/job/ace-recruit-agentic-ai-engineer","title":"Agentic AI Engineer","company":{"id":3800671,"name":"Ace Recruit","domain":"acerecruit.in","url":"https://alion.io/company/ace-recruit","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":20000,"max_usd":41000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":28},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Apache 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systems, calendars, and third-party services.\n\n- Build and maintain rigorous eval frameworks: offline eval sets, regression suites, latency benchmarks, and safety/guardrail tests.\n\n- Ship and operate LLMs in production: streaming responses, structured outputs, prompt versioning, cost/latency tracking, and observability.\n\n- Architect and maintain distributed microservices that expose LLM APIs async Python (FastAPI), event-driven pipelines, and real-time WebSocket workflows.\n\n- Build RAG pipelines: chunking strategies, embeddings, vector stores, retrieval tuning, and PHI-safe handling.\n\n- 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.\n\n- Collaborate closely with product, backend, and QA to define AI requirements and ship reliable, safe features.\n\nQualifications :\n\n- 3+ years of hands-on Gen AI / LLM engineering experience building and shipping real systems, not just research or prototypes.\n\n- 5+ years of hands-on AI / ML engineering experience overall (Gen AI + pre-Gen AI combined).\n\n- Deep LLM expertise in prompt engineering, tool/function calling, structured outputs, chain-of-thought, and agent orchestration (LangChain, LangGraph, or similar).\n\n- Proven eval culture that you treat evals as a first-class concern not an afterthought.\n\n- Production LLM experience, integrations with OpenAI / Azure OpenAI / AWS Bedrock, streaming, cost optimisation, and monitoring.\n\n- Strong software engineering fundamentals in Python (strong), async programming, REST APIs, CI/CD, Docker.\n\n- Distributed systems fluency with microservice design, event-driven architecture (Kafka/SQS/RabbitMQ), and LLM API gateway patterns.\n\n- End-to-end ownership mindset, you write tests, validate in staging, and don't consider a feature done until it's verified in production.\n\n- Preferred SkillsNot required for hiring but these make you stand out:\n\n- LLM post-training experience with fine-tuning, RLHF, DPO, or LoRA for domain-adapted or instruction-tuned models.\n\n- Automatic prompt optimisation familiarity with tools or techniques like DSPy, TextGrad, or automated prompt search for systematic prompt improvement.\n\n- Healthcare industry experience, working knowledge of EHR systems, HL7/FHIR standards, HIPAA compliance, or clinical workflows.\n\n- Startup experience where you've worked in a fast-moving, resource constrained environment where you wore multiple hats and shipped quickly.\n\n- Scaling & on-prem deployments experience deploying LLMs at scale, including self-hosted / on-prem model serving (vLLM, TGI, Triton) or hybrid cloud architectures.\n\nRequirements added by the job poster :\n\n- 5+ years of work experience with Applied Machine Learning\n\n- 3+ years of work experience with Generative AI\n\n- 3+ years of work experience with Python (Programming Language\nSkills\nGenerative AI, Agentic AI, Python, EHR/EMR, 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