{"id":2122351,"url":"https://alion.io/job/fujitsu-genai-application-developer-11313","title":"GenAI - Application Developer - 11313","company":{"id":896,"name":"Fujitsu","domain":"fujitsu.com","url":"https://alion.io/company/fujitsu-poland-sp-z-o-o","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":19500,"max_usd":45000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":26},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Embeddings","optional":false},{"name":"Git","optional":false},{"name":"LLM","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"SQL","optional":false},{"name":"Agile","optional":true},{"name":"AWS","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"Azure","optional":true},{"name":"Azure DevOps","optional":true},{"name":"CI/CD","optional":true},{"name":"Claude","optional":true},{"name":"Confluence","optional":true},{"name":"Docker","optional":true},{"name":"FastAPI","optional":true},{"name":"Flask","optional":true},{"name":"Function Calling","optional":true},{"name":"GCP","optional":true},{"name":"Gemini","optional":true},{"name":"GitHub","optional":true},{"name":"JavaScript","optional":true},{"name":"Jira","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"LlamaIndex","optional":true},{"name":"LLM Guardrails","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Node JS","optional":true},{"name":"OpenAI","optional":true},{"name":"OpenAI Agents SDK","optional":true},{"name":"PostgreSQL","optional":true},{"name":"RAGFlow","optional":true},{"name":"Scrum","optional":true},{"name":"SharePoint","optional":true},{"name":"Structured Outputs","optional":true},{"name":"Tool Use","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-10-09T00:04:58Z","employer_posted_date":"2026-10-09","last_verified_at":"2026-10-11T15:29:27Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"At Fujitsu, our purpose is to make the world more sustainable by building trust in society through innovation. Founded in Japan in 1935, Fujitsu has been a pioneer in technology and innovation for decades. Today, as a world-leading digital transformation partner, we are committed to transforming business and society in the digital age.\nWith approximately 130,000 employees across over 50 countries, Fujitsu offers a broad range of products, services, and solutions. We collaborate with our customers to co-create solutions that drive enterprise-wide digitalization while actively working to address social issues and contribute to the United Nations Sustainable Development Goals (SDGs).\nJob Title: GenAI - Application Developer - 11313\nLocation: Pune\nShift: 2:00 PM-11:00 PM\nExperience: 3-5 Years\nJob Description - AI Developer - GenAI / Agentic AI\nExperience: 3+ years\nFlexible based on hands-on fit.\nCandidates with strong AI project experience can also be considered.\nLocation / Shift:\nIndia / Remote / Hybrid / Work from Office as per project need\nClient shift may apply\nMulti-region team collaboration may be required\nRole Summary:\nYou will develop GenAI and Agentic AI solutions.\nYou will build AI assistants, RAG-based solutions, and agent workflows.\nYou will work with LLMs, prompts, APIs, tools, vector databases, and enterprise data sources.\nYou will support development, testing, deployment, and production support.\nYou will work with architects, senior developers, business teams, and delivery teams.\nPrimary Skills:\nMust Have:\nGenAI application development\nAgentic AI concepts and implementation\nPrompt engineering\nRAG implementation\nLLM API integration\nPython\nREST API development and integration\nSQL and basic data handling\nVector database and embeddings basics\nGit-based development\nGood to Have:\nLangChain / LangGraph / LlamaIndex \nOpenAI / Azure OpenAI / Claude / Gemini / AWS Bedrock\nAgent tools, function calling, and workflow orchestration\nModel Context Protocol \nFastAPI / Flask / Node.js\nDocker and basic CI/CD\nCloud basics: Azure / AWS / GCP\nLLM evaluation and observability basics\nResponsible AI and AI governance awareness\nKey Responsibilities:\n1) Requirement Understanding\nUnderstand business use cases for GenAI and Agentic AI solutions.\nClarify user needs, expected output, data sources, and workflow steps.\nUnderstand whether the solution needs chatbot, RAG, agent, automation, or decision-support capability.\nIdentify assumptions, dependencies, risks, and open points.\nWork with architect and senior developers to finalize technical approach.\nSupport estimation for assigned tasks.\n2) Solution Design\nSupport low-level design for assigned AI modules.\nDesign prompt flow, API flow, and response flow for assigned features.\nSupport RAG design using approved enterprise documents or databases.\nHelp define agent workflow steps, tools, fallback handling, and human review points.\nKeep design simple, secure, and easy to maintain.\nFollow architecture guidance and project standards.\n3) Development / Implementation\nDevelop GenAI features using Python or other approved technology stack.\nBuild LLM-based chat, search, summarization, classification, and Q&A features.\nDevelop RAG pipelines using embeddings, vector search, and retrieval logic.\nCreate and improve prompts for better response quality.\nBuild agent workflows that can call tools, APIs, or backend services.\nImplement structured outputs like JSON where required.\nWrite clean, readable, and maintainable code.\nFollow coding standards, branch process, and code review comments.\n4) Integration / Configuration\nIntegrate LLM APIs with application backend.\nConnect AI solutions with enterprise systems, APIs, files, databases, and knowledge sources.\nConfigure vector databases and document retrieval pipelines.\nConfigure environment variables, model settings, API keys, and service connections securely.\nSupport tool-use / function-calling implementation for agents.\nSupport integration with cloud services where needed.\nWork with DevOps and platform teams for environment setup.\n5) Testing & Validation\nTest prompts with different user scenarios.\nValidate RAG responses against source documents.\nPerform unit testing and integration testing for assigned components.\nTest agent workflows, tool calls, API calls, and fallback paths.\nValidate AI output for accuracy, relevance, safety, and consistency.\nFix defects found during testing and UAT.\nPrepare test evidence and validation notes.\n6) Performance Optimization\nImprove prompt quality and reduce unnecessary model calls.\nOptimize retrieval logic, chunking, metadata filters, and context usage.\nSupport response time and token usage optimization.\nTune API calls, retry logic, timeout, and caching where required.\nIdentify weak responses and suggest improvement actions.\nSupport cost-aware design and efficient execution.\n7) Security, Compliance & Governance\nFollow secure coding and data handling practices.\nUse only approved data sources and approved APIs.\nAvoid exposing API keys, tokens, passwords, or confidential data.\nSupport access control and audit logging as per design.\nFollow responsible AI guidelines for safe and reliable output.\nAdd guardrails and validation checks where required.\nEscalate data privacy or unsafe-output concerns early.\n8) Deployment & Release Management\nSupport deployment across Dev / Test / UAT / Prod environments.\nPrepare code changes for review and release.\nFollow Git and CI/CD process as per project setup.\nSupport release notes and deployment checklist preparation.\nPerform post-deployment validation.\nSupport rollback or quick fix activities when required.\n9) Production Support & RCA\nSupport production issues related to AI responses, APIs, retrieval, agents, and latency.\nCheck logs and identify basic failure reasons.\nDebug issues related to wrong answers, missing context, tool failure, or API errors.\nProvide RCA inputs for recurring issues.\nImplement fixes with proper testing.\nSupport hypercare after production release.\n10) Documentation & Knowledge Transfer\nPrepare technical notes for assigned AI components.\nDocument prompt behavior, API usage, RAG flow, tool flow, and configuration steps.\nMaintain test cases and validation results.\nUpdate support notes and runbooks where required.\nShare implementation details with team members.\nSupport knowledge transfer to QA, support, and delivery teams.\n11) Agile Delivery & Collaboration\nWork in Agile/Scrum delivery model.\nParticipate in daily stand-ups, sprint planning, reviews, and retrospectives.\nProvide clear daily updates on progress, blockers, and next steps.\nWork closely with AI architects, senior developers, QA, business analysts, and DevOps teams.\nTake ownership of assigned stories and deliver on time.\nRaise risks and blockers early.\nTools / Technologies\nCloud / Platform: Azure / AWS / GCP\nAI Tools: OpenAI, Azure OpenAI, Claude, Gemini, AWS Bedrock, local/open-source LLMs\nAgentic AI Tools: LangChain, LangGraph, LlamaIndex, Agents SDK, MCP \nData Tools: Vector databases, embeddings, document parsers, RAG pipeline\nDatabase / Warehouse: PostgreSQL, SQL Server, MongoDB, Vector DB\nProgramming Languages: Python, JavaScript / TypeScript, SQL\nAPI / Backend: FastAPI, Flask, Node.js, REST APIs\nDevOps Tools: Git, GitHub, Azure DevOps, Docker, CI/CD basics\nMonitoring Tools: Application logs, API logs, cloud monitoring, LLM evaluation logs\nDocumentation Tools: Jira, Confluence, SharePoint, Azure Boards\nQualification\nBE / BTech / MCA / MSc / BSc / BCA or equivalent practical experience\nAI / GenAI / Cloud / Python certification is good to have\nHands-on project experience in chatbot, RAG, LLM, AI agent, or automation use case is preferred\nSoft Skills\nClear communication\nStrong learning mindset\nOwnership of assigned work\nGood problem-solving ability\nTeam collaboration\nCuriosity to learn new AI tools\nGood documentation habit\nDelivery-focused mindset\nAt Fujitsu, we are committed to an inclusive recruitment process that values the diverse backgrounds and experiences of all applicants. 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