{"id":1285221,"url":"https://alion.io/job/saint-gobain-generative-aillm-application-engineer","title":"Generative AI/LLM Application Engineer","company":{"id":1756237,"name":"Saint-Gobain","domain":"saint-gobain.com","url":"https://alion.io/company/saint-gobain-com","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Alma Career","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":["Mumbai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":20000,"max_usd":42000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":27},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Chainlit","optional":false},{"name":"ElasticSearch","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Function Calling","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"React.js","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"Tokenization","optional":false},{"name":"Tool Use","optional":false},{"name":"Azure","optional":true},{"name":"Hybrid Search","optional":true},{"name":"JavaScript","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"OpenAI","optional":true},{"name":"Ragas","optional":true},{"name":"Reranking","optional":true}],"status":"live","first_seen_at":"2026-08-17T10:10:01Z","employer_posted_date":null,"last_verified_at":"2026-08-17T10:10:01Z","board_verified":false,"closed_at":null,"days_open":56,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":56},"description":"GenAI / LLM Application Engineer\n\nRole Summary :\n\nWe are looking for a GenAI / LLM Application Engineer to build production-grade AI assistants, retrieval systems, and workflow automation solutions. The role requires hands-on experience with multi-agent orchestration, RAG pipelines, search and retrieval infrastructure, backend APIs, and user-facing chatbot experiences.\n\nThe ideal candidate can take a business workflow, break it into AI-enabled subtasks, connect the right tools and services, and deliver reliable, measurable outcomes.\n\nKey Responsibilities :\n\n- Design and implement multi-agent orchestration systems that can decompose complex user requests into smaller tasks.\n\n- Build retrieval-augmented generation pipelines for enterprise documents and knowledge bases.\n\n- Develop and optimize search flows using vector search, semantic search, keyword search, and hybrid retrieval.\n\n- Integrate LLMs with external tools, APIs, and enterprise systems such as ticketing, HR, or support platforms.\n\n- Create backend services using Python and FastAPI to power AI features and APIs.\n\n- Work with LangChain and LangGraph to implement agent workflows, tool calling, and retrieval logic.\n\n- Build and maintain chatbot interfaces using React, Chainlit, or similar frontend frameworks.\n\n- Manage token limits, prompt quality, context handling, and conversation history strategies.\n\n- Evaluate response quality using human review, recall vs precision analysis, and automated evaluation methods.\n\n- Collaborate with product, backend, and business teams to translate workflows into AI solutions.\n\nRequired Skills :\n\n- Strong experience in Python backend development.\n\n- Hands-on experience with FastAPI or similar API frameworks.\n\n- Practical knowledge of LangChain and/or LangGraph.\n\n- Experience building RAG systems with chunking, embeddings, retrieval, and prompt assembly.\n\n- Familiarity with Elasticsearch or other vector/search databases.\n\n- Understanding of embeddings, transformers, attention, tokenization, and generation settings such as temperature, top-p, and top-k.\n\n- Ability to design prompt and context management strategies for longer conversations.\n\n- Experience integrating third-party APIs and enterprise tools.\n\n- Strong problem-solving ability and production-oriented engineering mindset.\n\nPreferred Skills :\n\n- Experience with MCP and tool-based agent architectures.\n\n- Knowledge of reranking, hybrid search, BM25, and HNSW.\n\n- Experience with Azure AI Search, Azure OpenAI, blob storage, or other Azure services.\n\n- Experience with React or Chainlit for AI chatbot interfaces.\n\n- Exposure to automated evaluation frameworks such as Ragas.\n\n- Familiarity with distributed systems, event-driven workflows, or microservices.\n\nQualifications :\n\n- 5+ years of software engineering experience, with at least part of that focused on AI or backend development.\n\n- Proven ability to ship features end to end, from backend logic to user-facing experience.\n\n- Experience working in production environments with measurable performance or quality outcomes.\nSkills\nAI Integration, LLM, Generative AI, Python, 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