{"id":2802935,"url":"https://alion.io/job/nasu-group-artificial-intelligence-developer-ragllm-models","title":"Artificial Intelligence Developer - RAG/LLM Models","company":{"id":3800363,"name":"Nasu Group","domain":"nasugroup.com","url":"https://alion.io/company/nasugroup","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":["Bengaluru, India","Chennai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":51000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":29},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Docker","optional":false},{"name":"FAISS","optional":false},{"name":"FastAPI","optional":false},{"name":"Function Calling","optional":false},{"name":"Git","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Neo4j","optional":false},{"name":"pgvector","optional":false},{"name":"Podman","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Tool Use","optional":false},{"name":"AutoGen","optional":true},{"name":"CrewAI","optional":true},{"name":"Google ADK","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"LiteLLM","optional":true},{"name":"LlamaIndex","optional":true},{"name":"Machine Learning","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"NLP","optional":true},{"name":"OpenAI Agents SDK","optional":true},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2026-07-27T09:48:22Z","employer_posted_date":null,"last_verified_at":"2026-07-27T09:48:22Z","board_verified":false,"closed_at":null,"days_open":84,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":84},"description":"Job Description :\n\nJoin a team building GenAI and agent-based applications for financial institutions from understanding complex systems to accelerating engineering delivery. Youll develop LLM-powered agents and retrieval systems that reason over large codebases and enterprise data to produce real engineering outcomes.\n\nRoles and Responsibilities :\n\n- Design, build and ship LLM-powered and agentic applications multi-step, tool-using, reliable.\n\n- Build retrieval-augmented (RAG) systems over large, messy enterprise data source code, documentation, schemas.\n\n- Work with vector databases and knowledge graphs for retrieval and reasoning.\n\n- Engineer prompts, evaluations and guardrails; measure and systematically improve output quality.\n\n- Integrate and route across multiple LLM providers; optimise for cost, latency, and on-prem / air-gapped constraints.\n\n- Build backend services and automation that turn model output into auditable, production-grade artefacts.\n\nMindset and problem-solving :\n\n- First-principles thinker decomposes ambiguous, open-ended problems and reasons from fundamentals rather than reaching for the nearest template.\n\n- Inventive solutioner proposes novel approaches, challenges assumptions, and weighs trade-offs to find the best answer, not just a working one.\n\n- Bias to prototype experiments quickly, learns from what the models and data actually do, and iterates.\n\n- Strong analytical ability and genuine curiosity; 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