GenAI - Application Developer - 11313

Job description

Job Description GenAI - Application Developer - 11313 Job Location: Pune Location Flexibility: Primary Location Only Req Id: 11313 Posting Start Date: 8/26/26 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. With 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). Job Title: GenAI - Application Developer - 11313 Location: Pune Shift: 2:00 PM-11:00 PM Experience: 3-5 Years Job Description – AI Developer - GenAI / Agentic AI Experience: 3+ years Flexible based on hands-on fit. Candidates with strong AI project experience can also be considered. Location / Shift: • India / Remote / Hybrid / Work from Office as per project need • Client shift may apply Multi-region team collaboration may be required Role Summary: You will develop GenAI and Agentic AI solutions. You will build AI assistants, RAG-based solutions, and agent workflows. You will work with LLMs, prompts, APIs, tools, vector databases, and enterprise data sources. You will support development, testing, deployment, and production support. You will work with architects, senior developers, business teams, and delivery teams. Primary Skills: Must Have: • GenAI application development • Agentic AI concepts and implementation • Prompt engineering • RAG implementation • LLM API integration • Python • REST API development and integration • SQL and basic data handling • Vector database and embeddings basics • Git-based development Good to Have: • LangChain / LangGraph / LlamaIndex • OpenAI / Azure OpenAI / Claude / Gemini / AWS Bedrock • Agent tools, function calling, and workflow orchestration • Model Context Protocol • FastAPI / Flask / Node.js • Docker and basic CI/CD • Cloud basics: Azure / AWS / GCP • LLM evaluation and observability basics Responsible AI and AI governance awareness Key Responsibilities: 1) Requirement Understanding • Understand business use cases for GenAI and Agentic AI solutions. • Clarify user needs, expected output, data sources, and workflow steps. • Understand whether the solution needs chatbot, RAG, agent, automation, or decision-support capability. • Identify assumptions, dependencies, risks, and open points. • Work with architect and senior developers to finalize technical approach. Support estimation for assigned tasks. 2) Solution Design • Support low-level design for assigned AI modules. • Design prompt flow, API flow, and response flow for assigned features. • Support RAG design using approved enterprise documents or databases. • Help define agent workflow steps, tools, fallback handling, and human review points. • Keep design simple, secure, and easy to maintain. Follow architecture guidance and project standards. 3) Development / Implementation • Develop GenAI features using Python or other approved technology stack. • Build LLM-based chat, search, summarization, classification, and Q&A features. • Develop RAG pipelines using embeddings, vector search, and retrieval logic. • Create and improve prompts for better response quality. • Build agent workflows that can call tools, APIs, or backend services. • Implement structured outputs like JSON where required. • Write clean, readable, and maintainable code. Follow coding standards, branch process, and code review comments. 4) Integration / Configuration • Integrate LLM APIs with application backend. • Connect AI solutions with enterprise systems, APIs, files, databases, and knowledge sources. • Configure vector databases and document retrieval pipelines. • Configure environment variables, model settings, API keys, and service connections securely. • Support tool-use / function-calling implementation for agents. • Support integration with cloud services where needed. Work with DevOps and platform teams for environment setup. 5) Testing & Validation • Test prompts with different user scenarios. • Validate RAG responses against source documents. • Perform unit testing and integration testing for assigned components. • Test agent workflows, tool calls, API calls, and fallback paths. • Validate AI output for accuracy, relevance, safety, and consistency. • Fix defects found during testing and UAT. Prepare test evidence and validation notes. 6) Performance Optimization • Improve prompt quality and reduce unnecessary model calls. • Optimize retrieval logic, chunking, metadata filters, and context usage. • Support response time and token usage optimization. • Tune API calls, retry logic, timeout, and caching where required. • Identify weak responses and suggest improvement actions. Support cost-aware design and efficient execution. 7) Security, Compliance & Governance • Follow secure coding and data handling practices. • Use only approved data sources and approved APIs. • Avoid exposing API keys, tokens, passwords, or confidential data. • Support access control and audit logging as per design. • Follow responsible AI guidelines for safe and reliable output. • Add guardrails and validation checks where required. Escalate data privacy or unsafe-output concerns early. 8) Deployment & Release Management • Support deployment across Dev / Test / UAT / Prod environments. • Prepare code changes for review and release. • Follow Git and CI/CD process as per project setup. • Support release notes and deployment che