{"id":4321437,"url":"https://alion.io/job/unosecur-ai-ml-engineer","title":"AI / ML Engineer","company":{"id":3914,"name":"Unosecur","domain":"unosecur.com","url":"https://alion.io/company/unosecur","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Join","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"GCP","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"RAG","optional":false},{"name":"Vertex AI","optional":false},{"name":"Ragas","optional":true}],"status":"live","first_seen_at":"2026-11-14T20:36:45Z","employer_posted_date":null,"last_verified_at":"2026-11-14T20:36:45Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"You will sit on the Engineering team and own the design and development of Unosecur's Generative AI and agentic capabilities, including LLM-backed services, agent orchestration, and the architecture connecting AI systems to the security platform. You'll work closely with the founding and senior engineering team and partner with backend, cloud, and product teams.\n\nThe candidate will have responsibilities across the following functions: \n\nGenAI and Agentic Systems: \nDesign and architect end-to-end GenAI and agentic systems, from problem framing through orchestration, retrieval, and production deployment.\nBuild agentic workflows using LangChain, LangGraph, and MCP.\nIntegrate tools, plugins, and external data sources with clear permission and failure boundaries.\n\nRAG and Retrieval: \nBuild and continuously improve RAG pipelines.\nEvaluate retrieval quality and improve retrieval performance.\nWork with vector and embedding databases.\n\nModel Strategy: \nOwn model-selection strategy across LLMs and SLMs.\nBalance model quality, latency, cost, security, and data-handling requirements.\n\nEngineering and Reliability: \nBuild reliable and scalable AI services capable of running at production load.\nImprove observability, quality, and reliability across AI systems.\nPartner with Product and Security teams to translate ambiguous problems into safe, well-architected AI capabilities.\n\nTechnical Leadership: \nMentor engineers across GenAI initiatives.\nAct as the technical reference point for Generative AI within the engineering team.\n\nRequirements: \n10+ years of overall engineering experience, including 3+ years of hands-on experience with Generative AI / LLM-based solutions.\nDemonstrated experience designing and architecting AI solutions, not just implementing or consuming AI tools.\nAbility to demonstrate AI systems personally designed and built.\nStrong expertise in the GenAI/agentic stack, including: LangChain, LangGraph, MCP, RAG, vector databases, Multiple LLMs / SLMs.\nStrong engineering fundamentals, including: Data structures, problem-solving, scalable distributed services\nWorking knowledge of GenAI platforms such as AWS Bedrock, Google Vertex AI, or similar.\nExperience with AWS or GCP and cloud security fundamentals.\nStrong ownership and builder mindset.\nComfortable operating in startup environments with ambiguity.\nStrong judgment when making architecture and technical trade-offs.\n\nNice to Have: \nOpen-source AI contributions or substantial hands-on experience with open-source AI projects.\nExperience building AI into security, identity, or compliance-sensitive products.\nExperience with RAG evaluation tooling such as Ragas.\nStructured approaches to measuring and improving AI quality.\nProduct-based or startup experience with end-to-end system ownership.\nSkills\nAWS, GCP, Gen AI, Generative AI, Google Cloud, LLM, LLMs, LangChain, LangGraph, MCP, 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