{"id":61768,"url":"https://alion.io/job/bupa-senior-ai-developer","title":"Senior AI Developer","company":{"id":973,"name":"Bupa","domain":"bupa.com","url":"https://alion.io/company/bupa","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","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":["Gurgaon, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":22000,"max_usd":48000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":29},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FAISS","optional":false},{"name":"FastAPI","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Milvus","optional":false},{"name":"OpenAI","optional":false},{"name":"PEFT","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Weaviate","optional":false},{"name":"Fine-tuning","optional":true},{"name":"Hugging Face","optional":true},{"name":"Hybrid Search","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"Kubeflow","optional":true},{"name":"LlamaIndex","optional":true},{"name":"LoRA","optional":true},{"name":"MLFlow","optional":true},{"name":"NIST AI RMF","optional":true},{"name":"OWASP","optional":true},{"name":"PyTorch","optional":true},{"name":"QLoRA","optional":true},{"name":"Red Teaming","optional":true},{"name":"Rest API","optional":true},{"name":"TensorFlow","optional":true},{"name":"Terraform","optional":true},{"name":"Transformers","optional":true}],"status":"closed","first_seen_at":"2026-08-18T08:21:18Z","employer_posted_date":null,"last_verified_at":"2026-10-17T08:21:38Z","board_verified":false,"closed_at":"2026-10-17T08:21:38Z","days_open":60,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":60},"description":"We are looking for an experienced Lead AI Engineer to lead the design and development of production-grade generative AI, LLM, RAG, and agentic AI solutions. The ideal candidate should have strong hands-on experience in Python, LLMs, RAG architecture, AI agents, LangChain/LangGraph, vector databases, cloud, and MLOps, along with the ability to design scalable AI systems and mentor engineering teams.\nResponsibilities:\nDesign and develop scalable GenAI and LLM-powered applications for enterprise use cases.\nArchitect and implement advanced RAG pipelines, including document processing, embeddings, retrieval, re-ranking, and evaluation.\nBuild agentic AI and multi-agent systems using frameworks such as LangChain, LangGraph, AutoGen, or Semantic Kernel.\nIntegrate LLMs such as OpenAI, Azure OpenAI, Claude, Gemini, or open-source LLMs.\nDevelop AI services and microservices using Python and FastAPI.\nWork with vector databases such as FAISS, Pinecone, Weaviate, Milvus, or Azure AI Search.\nImplement prompt engineering, function/tool calling, structured outputs, memory, and agent orchestration.\nBuild and maintain LLM evaluation and monitoring frameworks to improve accuracy, relevance, safety, and reliability.\nDeploy AI solutions on Azure/AWS/GCP using Docker and Kubernetes.\nEstablish and improve MLOps/LLMOps pipelines, CI/CD, model monitoring, and production deployment practices.\nWork on AI security, responsible AI, guardrails, prompt injection prevention, and LLM safety.\nDrive technical architecture, code reviews, engineering best practices, and system design.\nMentor engineers and provide technical leadership across AI initiatives.\nRequirements:\n8-12 years of software/AI engineering experience.\nStrong Python programming.\nHands-on generative AI/LLM experience.\nStrong RAG architecture and implementation experience.\nExperience with Agentic AI / AI Agents / Multi-Agent Systems.\nGood to have: LangChain / LangGraph or equivalent agent frameworks.\nExperience with Vector Databases.\nNice to have: LLM APIs such as OpenAI / Azure OpenAI / Claude / Gemini.\nExperience in FastAPI / REST APIs / Microservices, Docker, and Kubernetes.\nExperience with at least one major cloud platform: Azure, AWS, or GCP.\nUnderstanding of MLOps / LLMOps.\nStrong knowledge of AI system architecture and scalability.\nGood technical leadership and mentoring experience.\nGood to have: LlamaIndex, AutoGen, Semantic Kernel, Hugging Face Transformers, PyTorch / TensorFlow, MLflow / Kubeflow, Terraform and CI/CD, LLM evaluation and observability tools, AI red teaming / AI security, OWASP LLM Top 10 / NIST AI RMF, Fine-tuning / LoRA / QLoRA.\nKnowledge graphs and hybrid search.","description_format":"text","description_chars":2665,"description_truncated":false,"requirements":{"experience_years_min":8,"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":["Hospitals & Health Systems","Nursing Homes & Long-Term Care","Health Insurance & 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