{"id":3308891,"url":"https://alion.io/job/bloom-ai-full-stack-ai-engineering-lead","title":"Full Stack AI Engineering Lead","company":{"id":700788,"name":"Bloom AI","domain":"bloomai.co","url":"https://alion.io/company/bloom-ai-2","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":"lead","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Delhi, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":24000,"max_usd":63000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":47},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"CrewAI","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Next.js","optional":false},{"name":"OpenAI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"React.js","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Search","optional":false},{"name":"SQL","optional":false},{"name":"Amazon Neptune","optional":true},{"name":"GraphRAG","optional":true},{"name":"JavaScript","optional":true},{"name":"Langfuse","optional":true},{"name":"LangSmith","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Neo4j","optional":true}],"status":"live","first_seen_at":"2026-10-26T22:58:56Z","employer_posted_date":null,"last_verified_at":"2026-10-26T22:58:56Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"About Bloom AI\n\nBloom AI builds AI-native intelligence systems and workflows for enterprise teams across research, marketing, sales and other knowledge-intensive functions. We combine enterprise data, external intelligence, AI and software engineering to help organizations build systems that continuously understand context, generate intelligence and support action. Our model sits between traditional product companies and services firms. We build reusable technology and engineering patterns while deploying them against real enterprise problems and adapting them to different client environments.\n\nBloom AI is based in the U.S. and India (Delhi/NCR). This is a hybrid role.\n\nThe Role\n\nWe are looking for a hands-on Full Stack AI Engineer with 4–6 years of experience who can design, build and deploy production-grade AI applications, with particular emphasis on multi-agent systems, backend engineering and end-to-end application development.\n\nThis is a core engineering role for someone who combines strong software fundamentals with practical experience building LLM-powered applications, agentic workflows and intelligent data systems.\n\nYou should be comfortable working across the stack—from building agent orchestration and backend services to developing user-facing applications, integrating enterprise data and deploying solutions in cloud environments.\n\nThis is not a prompt-engineering or research-only role. We are looking for someone who can turn AI capabilities into reliable, scalable software that solves real business problems.\n\nYou will work across client engagements, reusable Bloom AI capabilities and internal technology development, taking ownership of solutions from architecture through production deployment. You will be part of core team reporting to the CEO. \n\nWhat You'll Do\n\nBuild Agentic Systems\n\nDesign and develop multi-agent systems that coordinate specialized agents to execute complex tasks and workflows.\nImplement agent orchestration, planning, tool calling, task delegation, context management and state management.\nBuild LLM-powered applications using frameworks such as LangGraph, LangChain, CrewAI, AutoGen or similar.\nDevelop retrieval-augmented generation (RAG), semantic search, agent memory and knowledge retrieval capabilities.\nIntegrate agents with enterprise APIs, databases, documents and external data sources.\nImplement evaluation, tracing, error handling, monitoring and reliability mechanisms for production AI systems.\n\nBuild Backend and Full Stack Applications\nDesign and develop scalable backend services, REST APIs and application logic using Python and frameworks such as FastAPI.\nBuild user-facing applications using modern frontend frameworks such as React, Next.js or similar.\nDevelop data models, database integrations and processing pipelines for structured and unstructured data.\nImplement asynchronous processing, background jobs and integrations between application components.\nDesign modular, maintainable architectures that support reuse across client environments.\nOwn features end-to-end, including technical design, development, testing, integration and deployment.\n\nWork with Graph and Context Systems\nDesign data structures and retrieval mechanisms that help AI systems understand entities, relationships and context.\nWork with graph-based data models and knowledge graphs to represent relationships across enterprise and external data.\nCombine structured data, semantic search and graph-based retrieval to support richer agent reasoning and intelligence workflows.\nContribute to the development of persistent context, memory and state-management capabilities for AI agents.\nEvaluate when relational databases, vector databases or graph-based approaches are appropriate for different problems.\n\nDeploy Production Grade Systems\nDeploy and manage applications on cloud infrastructure, preferably Microsoft Azure, with AWS or GCP experience also relevant.\nWork with Azure OpenAI, Azure AI Foundry, model APIs and enterprise AI services.\nImplement containerization, CI/CD, authentication, logging and application monitoring.\nBuild systems with appropriate security, access controls, data privacy and reliability standards.\nDebug performance, integration and production issues across the full application stack.\n\nSolve Real Enterprise Problems\nWork with business and technical teams to understand workflows and translate requirements into working AI solutions.\nRapidly prototype new capabilities, evaluate technical approaches and move successful prototypes into production.\nIntegrate AI systems with existing enterprise applications, data sources and workflows.\nCollaborate with engineers to improve architecture, code quality and reusable components.\nParticipate in client discussions, technical demonstrations and solution design.\n\nWhat We're Looking For\n4–6 years of professional software engineering experience, including hands-on development of AI/LLM applications.\nStrong proficiency in Python, backend engineering, API development and software architecture.\nPractical experience building agentic or multi-agent systems, beyond basic chatbot or prompt-based applications.\nExperience with agent orchestration frameworks such as LangGraph, LangChain, CrewAI, AutoGen or similar.\nStrong understanding of LLM APIs, tool calling, RAG, embeddings, vector databases and context management.\nExperience developing full-stack applications, including working knowledge of React, Next.js or another modern frontend framework.\nStrong SQL, database design and data integration fundamentals.\nWorking understanding of graph data structures, entity-relationship modeling and graph-based retrieval concepts.\nExperience taking applications from development through testing and production deployment.\nFamiliarity with cloud infrastructure, Docker, Git, CI/CD and modern engineering practices.\nStrong problem-solving, debugging and independent execution skills.\n\nGood to Have\n\nStart-up or 0->1 experience. \nExperience with Microsoft Azure, Azure AI Foundry or Azure OpenAI.\nHands-on experience with Neo4j, Amazon Neptune or other graph databases, including knowledge graph construction, Cypher and GraphRAG.\nExperience building stateful agents, long-running workflows, persistent memory or context-management systems.\nFamiliarity with agent evaluation, observability and tracing tools such as LangSmith, Langfuse or similar.\nExperience with Model Context Protocol (MCP), event-driven architectures, queues or distributed systems.\nExperience integrating AI into enterprise applications such as CRM, marketing, sales or research workflows.\nExperience working directly with enterprise clients or in a fast-moving 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