GenAI - Application Developer - 11313

Fujitsu Limited; Pune District; Hybrid; INR 1,500,000 - 2,300,000; Full time

Job summary

Fujitsu Limited in Pune invites experienced GenAI Application Developers to join our multi-regional AI delivery teams. You will design and implement GenAI and Agentic AI solutions, build AI assistants, RAG pipelines, and agent workflows, working with LLMs, prompts, APIs, and vector databases.

Collaborate with architects, senior developers, and business teams, contribute to testing, deployment, and production support, and follow secure coding practices and governance.

Qualifications

• BE / BTech / MCA / MSc / BSc / BCA or equivalent practical experience.
• AI / GenAI / Cloud / Python certification is good to have.
• Hands-on project experience in chatbot, RAG, LLM, AI agent, or automation use case is preferred.

Responsibilities

• Understand business use cases for GenAI and Agentic AI solutions.
• Clarify user needs, 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.
• Design prompt flow, API flow, and response flow for assigned features.
• Support RAG design using approved enterprise docs 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.
• Develop GenAI features using Python or other approved tech.
• 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.
• 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.
• Test prompts with different user scenarios.
• 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.
• 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.
• 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.
• Prepare code changes for review and release.
• Follow Git and CI/CD process as per project setup.
• Support release notes and deployment checklist preparation.
• Support rollback or quick fix activities when required.
• Support production issues related to AI responses, APIs, retrieval, agents, and latency.
• Check logs and identify basic failure reasons.
• Debug issues related to wrong answers, missing context, tool failure, or API errors.
• Provide RCA inputs for recurring issues.
• Implement fixes with proper testing.
• Support hypercare after production release.
• Prepare technical notes for assigned AI components.
• Document prompt behavior, API usage, RAG flow, tool flow, and configuration steps.
• Maintain test cases and validation results.
• Share implementation details with team members.
• Support knowledge transfer to QA, support, and delivery teams.
• Work in Agile/Scrum delivery model.
• Participate in daily stand-ups, sprint planning, reviews, and retrospectives.
• Provide clear daily updates on progress, blockers, and next steps.
• Work closely with AI architects, senior developers, QA, business analysts, and DevOps teams.
• Take ownership of assigned stories and deliver on time.
• Raise risks and blockers early.

Skills

Agentic AI concepts

RAG implementation

LLM API integration

Python

REST API development

SQL handling

Vector embeddings basics

Git-based development

Education

BE / BTech / MCA / MSc / BSc / BCA or equivalent

Tools

LangChain

Vector DB

Docker

CI/CD basics

FastAPI/Flask/Node.js

Job description

Location Flexibility: Primary Location Only

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:

• Agentic AI concepts and implementation
• 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:

• Agent tools, function calling, and workflow orchestration
• FastAPI / Flask / Node.js
• Docker and basic CI/CD
• LLM evaluation and observability basics
• Responsible AI and AI governance awareness

Key Responsibilities:

• 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.
• 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

• Prepare code changes for review and release.
• Follow Git and CI/CD process as per project setup.
• Support release notes and deployment checklist preparation.
• Support rollback or quick fix activities when required.

9) Production Support & RCA

• Support production issues related to AI responses, APIs, retrieval, agents, and latency.
• Check logs and identify basic failure reasons.
• Debug issues related to wrong answers, missing context, tool failure, or API errors.
• Provide RCA inputs for recurring issues.
• Implement fixes with proper testing.
• Support hypercare after production release.

10) Documentation & Knowledge Transfer

• Prepare technical notes for assigned AI components.
• Document prompt behavior, API usage, RAG flow, tool flow, and configuration steps.
• Maintain test cases and validation results.
• Update support notes and runbooks where required.
• Share implementation details with team members.
• Support knowledge transfer to QA, support, and delivery teams.

11) Agile Delivery & Collaboration

• Work in Agile/Scrum delivery model.
• Participate in daily stand-ups, sprint planning, reviews, and retrospectives.
• Provide clear daily updates on progress, blockers, and next steps.
• Work closely with AI architects, senior developers, QA, business analysts, and DevOps teams.
• Take ownership of assigned stories and deliver on time.
• Raise risks and blockers early.

Tools / Technologies

• Agentic AI Tools: LangChain, LangGraph, LlamaIndex, Agents SDK, MCP
• Data Tools: Vector databases, embeddings, document parsers, RAG pipeline
• Database / Warehouse: PostgreSQL, SQL Server, MongoDB, Vector DB

Qualification

• BE / BTech / MCA / MSc / BSc / BCA or equivalent practical experience
• AI / GenAI / Cloud / Python certification is good to have
• Hands-on project experience in chatbot, RAG, LLM, AI agent, or automation use case is preferred

Soft Skills

• Clear communication
• Strong learning mindset
• Ownership of assigned work
• Good problem-solving ability
• Team collaboration
• Curiosity to learn new AI tools
• Good documentation habit

Delivery-focused mindset

At Fujitsu, we are committed to an inclusive recruitment process that values the diverse backgrounds and experiences of all applicants. We believe that hiring people from a wide variety of backgrounds makes us stronger, not because it's the right thing to do, but because it allows us to draw on a wider range of perspectives and life experiences.