{"id":3090837,"url":"https://alion.io/job/attributics-genai-developer","title":"GenAI Developer","company":{"id":9121,"name":"Attributics","domain":"attributics.com","url":"https://alion.io/company/attributics","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":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":10000,"max_usd":25000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":18},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AutoGen","optional":false},{"name":"Azure","optional":false},{"name":"Claude","optional":false},{"name":"CrewAI","optional":false},{"name":"Embeddings","optional":false},{"name":"FAISS","optional":false},{"name":"Function Calling","optional":false},{"name":"Gemini","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"Llama","optional":false},{"name":"LLM","optional":false},{"name":"Milvus","optional":false},{"name":"OpenAI","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"Qdrant","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Search","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Weaviate","optional":false},{"name":"FastAPI","optional":true},{"name":"Flask","optional":true},{"name":"Hugging Face","optional":true},{"name":"PyTorch","optional":true},{"name":"Rest API","optional":true},{"name":"TensorFlow","optional":true},{"name":"Transformers","optional":true}],"status":"live","first_seen_at":"2026-10-22T14:35:30Z","employer_posted_date":null,"last_verified_at":"2026-10-22T14:35:30Z","board_verified":false,"closed_at":null,"days_open":5,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":5},"description":"We are looking for a GenAI Developer with 2-4 years of experiencein building AI-powered applications using Large Language Models (LLMs). The ideal candidate should have hands-on experience developing Retrieval-Augmented Generation (RAG)applications, AI Agents, Vector Databases, and orchestration frameworks such as LangChain or LangGraph. You will work closely with AI Engineers, Data Engineers, and Product teams to build scalable, production-ready AI solutions for enterprise marketing, customer engagement, and decision intelligence platforms.\n\nResponsibilities: \nDesign, develop, and deploy enterprise-grade Generative AI applications.\nBuild Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models.\nDevelop AI Agents capable of reasoning, tool calling, workflow automation, and multi-step task execution.\nBuild and orchestrate AI workflows using LangChain, LangGraph, or similar frameworks.\nIntegrate Large Language Models (OpenAI, Anthropic, Gemini, Llama, etc. ) into production applications.\nDesign and optimise vector search pipelines using modern Vector Databases.\nEvaluate, monitor, and optimise LLM performance, latency, accuracy, and cost.\nParticipate in architecture discussions and contribute to the design of scalable AI platforms.\n\nRequirements: \n2-4 years of software development experience, with hands-on experience in Generative AI.\nStrong proficiency in Python.\nHands-on experience building Retrieval-Augmented Generation (RAG)applications.\nExperience with LangChain, LangGraph, CrewAI, AutoGen, or similar orchestration frameworks.\nStrong understanding of AI Agents, tool calling, function calling, and agent workflows.\nExperience working with vector databases such as Pinecone, Weaviate, Qdrant, ChromaDB, FAISS, or Milvus.\nStrong understanding of embeddings, semantic search, and document retrieval.\nExperience integrating APIs and LLM providers such as OpenAI, Azure OpenAI, Anthropic Claude, Gemini, or open-source models.\nGood understanding of prompt engineering, structured outputs, and LLM evaluation.\nStrong problem-solving and analytical skills.\n\nPreferred Skills: \nExperience with NVTabular and feature engineering pipelines.\nExperience with PyTorch, TensorFlow, or Hugging Face Transformers.\nExperience building REST APIs using FastAPI or Flask.\n\nGood to Have: \nStrong passion for Artificial Intelligence and emerging GenAI technologies.\nAbility to design scalable, production-ready AI systems rather than just prototypes.\nCuriosity to experiment with new LLM frameworks and agent architectures.\nStrong debugging and problem-solving abilities.\nExcellent communication and collaboration skills.\nAbility to work in a fast-paced, innovation-driven environment.\nImmediate joiners preferred.\nSkills\nautogen, crewai, langchain, langgraph, orchestration, python, rag, retrieval-augmented generation","description_format":"text","description_chars":2859,"description_truncated":false,"requirements":{"experience_years_min":2,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Marketing Automation","Decision Intelligence","Marketing & Sales AI","Customer 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