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 Description – AI Technical Consultant

AI Agents | GenAI | RAG | APIs | Enterprise Integration | AI Accelerators

Role Title: AI Technical Consultant

Experience:

• 4–8 years of relevant professional experience.
• Experience in software engineering, AI/ML engineering, data engineering, or automation roles.
• Hands-on experience in developing LLM-based or AI-enabled solutions.
• Experience in delivering production or near-production AI applications is preferred.

Role Summary:

This approach combines AI-native ways of working, strong business understanding, and trust-led transformation.

We are looking for an AI Technical Consultant who can design and build intelligent AI agents and client-facing accelerators.

The candidate will work across:

• Large Language Models
• Agentic AI
• Retrieval-Augmented Generation
• APIs and microservices
• Enterprise data sources
• Cloud platforms
• Consulting workflows

The role requires strong technical development and client collaboration skills.

The candidate should be able to understand a business problem and quickly convert it into a practical AI solution.

The candidate will also be responsible for taking solutions from initial idea and prototype to deployment and operational support.

Role Objectives:

The AI Technical Consultant will:

• Own and lead the development of AI agents used by consultants.
• Build reusable AI accelerators for client engagements.
• Convert business requirements into practical AI solutions.
• Integrate AI agents with enterprise systems and data sources.
• Support rapid prototyping and production-ready implementation.
• Balance delivery speed, engineering quality, security, and business value.
• Promote responsible and controlled use of AI.

Primary Skills:

• Python and FastAPI
• TypeScript or Node.js
• REST API and GraphQL development
• Large Language Models
• Agentic AI development
• Retrieval-Augmented Generation
• Prompt engineering
• LangChain
• Microsoft Agent Framework
• OpenAI SDK
• Azure AI SDK
• Microsoft Copilot Studio
• Vector databases
• Enterprise system integration
• Docker
• Cloud platforms
• Git and CI/CD

Key Responsibilities:

1. AI Agent Design and Development

• Design and build intelligent AI agents for consulting and business workflows.
• Develop AI agents using platforms and frameworks such as:
• Microsoft Copilot Studio
• Azure AI
• LangChain
• Microsoft Agent Framework
• OpenAI SDK
• Claude
• Gemini
• Llama
• Build single-agent and multi-agent solutions based on business needs.
• Define agent goals, tasks, tools, memory, and decision logic.
• Develop reusable agent components and templates.
• Implement tool-calling and workflow execution.
• Ensure agents provide accurate, relevant, and controlled responses.
• Maintain human validation for important business decisions.

2. AI Accelerator Development

• Build reusable AI accelerators for client engagements.
• Develop tools that improve consultant productivity.
• Support AI accelerators for areas such as:
• research and document analysis
• proposal development
• knowledge discovery
• data analysis
• business process support
• decision support
• project delivery automation
• Convert proof of concepts into scalable applications.
• Maintain reusable code, APIs, templates, and implementation guides.
• Ensure accelerators can be configured for different clients and use cases.

3. Prompt Engineering and AI Orchestration

• Design clear and effective prompts for different business use cases.
• Develop system prompts, task prompts, and reusable prompt templates.
• Build prompt chaining and workflow orchestration.
• Develop tool-calling and function-calling logic.
• Manage conversation context and memory.
• Implement structured output formats.
• Validate AI responses against required schemas.
• Improve prompt quality through regular testing and evaluation.
• Reduce incorrect or unsupported responses.

4. RAG Solution Development

• Design and implement Retrieval-Augmented Generation pipelines.
• Connect LLMs with enterprise documents and knowledge sources.
• Build document ingestion and preprocessing pipelines.
• Define suitable document chunking strategies.
• Generate, store, and manage embeddings.
• Work with vector search platforms such as:
• Qdrant
• Pinecone
• FAISS
• Azure AI Search
• equivalent vector database platforms
• Implement metadata filtering and hybrid search.
• Apply reranking methods where required.
• Improve retrieval relevance and response accuracy.
• Maintain source references and traceability.
• Ensure answers are grounded in approved information.

5. Enterprise Data Integration

• Connect AI solutions with enterprise data sources such as:
• SharePoint
• CRM platforms
• relational databases
• NoSQL databases
• cloud storage
• business applications
• REST APIs
• GraphQL services
• Build secure and reliable integration workflows.
• Handle structured and unstructured data.
• Implement authentication and authorisation.
• Manage data transformation between source systems and AI applications.
• Ensure smooth and secure data flow across platforms.
• Handle API failures, data quality issues, and integration errors.

6. Backend and API Development

• Build scalable backend services using Python and FastAPI.
• Develop services using TypeScript or Node.js where required.
• Create REST and GraphQL APIs.
• Expose AI agent and RAG capabilities through secure APIs.
• Implement request and response validation.
• Implement exception handling, retry logic, and logging.
• Develop asynchronous processing where required.
• Prepare clear API documentation.
• Build reusable services for multiple applications and accelerators.
• Optimise APIs for performance and scalability.

7. AI-Assisted Software Development

• Use modern AI-assisted coding tools to improve development speed.
• Work with approved tools such as:
• GitHub Copilot
• Claude Code
• Gemini
• other AI development assistants
• Review and validate all AI-generated code.
• Ensure generated code follows quality and security standards.
• Use AI tools for:
• code generation
• code review
• unit test creation
• technical documentation
• troubleshooting
• refactoring
• Maintain human review and approval for important code changes.

8. LLM Platform Integration

• Integrate applications with approved commercial and open-source LLMs.
• Work with models and services such as:
• Azure OpenAI
• OpenAI
• Anthropic Claude
• Google Gemini
• Llama
• other approved models
• Select suitable models based on:
• business use case
• accuracy
• latency
• security
• privacy
• cost
• Implement model fallback and routing where required.
• Monitor token usage, latency, and API cost.
• Handle rate limits and service errors.
• Support private, cloud, and hybrid deployment models.

9. AI Evaluation and Quality Assurance

• Define test scenarios for AI agents and RAG applications.
• Evaluate response:
• accuracy
• relevance
• completeness
• consistency
• safety
• source grounding
• Test retrieval quality and source relevance.
• Create benchmark datasets for repeated evaluation.
• Perform regression testing after changes to prompts, models, data, or workflows.
• Analyse failure patterns and improve the solution.
• Track evaluation results and quality improvements.
• Ensure the solution meets agreed business acceptance criteria.
• Collect and use stakeholder feedback for continuous improvement.

10. Responsible AI, Guardrails, and Security

• Implement guardrails for safe and controlled AI usage.
• Protect confidential and sensitive information.
• Apply role-based access controls.
• Follow least-privilege access principles.
• Implement input and output validation.
• Support content filtering and prompt-injection protection.
• Prevent unauthorised access to enterprise data.
• Maintain audit logs where required.
• Follow responsible AI guidelines.
• Ensure human review for sensitive or high-impact outputs.
• Support data privacy, retention, and compliance requirements.

11. Cloud and Container Deployment

• Package applications using Docker.
• Create and maintain Dockerfiles and container images.
• Deploy AI solutions on Azure, AWS, or GCP.
• Manage application configurations and environment variables.
• Use secure secrets-management services.
• Support development, test, staging, and production environments.
• Troubleshoot deployment and runtime issues.
• Support scaling and availability requirements.
• Monitor container health and application performance.

12. CI/CD and Engineering Practices

• Use Git for source-code management.
• Follow suitable branching and pull-request practices.
• Build and maintain CI/CD pipelines using:
• GitHub Actions
• Azure DevOps
• other approved pipeline tools
• Automate:
• application build
• unit testing
• integration testing
• security scanning
• container creation
• deployment
• Maintain environment-specific configurations.
• Support code reviews and release validation.
• Prepare deployment and rollback plans.
• Ensure changes are traceable and properly documented.

13. Client Consulting and Business Collaboration

• Work closely with consultants and client stakeholders.
• Understand business problems and expected outcomes.
• Convert business needs into technical solution options.
• Explain AI concepts in simple business language.
• Conduct discovery sessions and solution workshops.
• Prepare and deliver solution demonstrations.
• Support AI use-case assessment and prioritisation.
• Share realistic estimates, assumptions, risks, and dependencies.
• Manage stakeholder expectations clearly.
• Focus on measurable business value and practical adoption.
• Avoid building technology solutions without a clear business purpose.

14. Solution Architecture and Technical Design

• Prepare high-level and detailed technical designs.
• Define:
• application architecture
• integration architecture
• data architecture
• AI architecture
• deployment architecture
• Select suitable models, frameworks, databases, and cloud services.
• Consider security, performance, scalability, maintainability, and cost.
• Identify technical risks and mitigation actions.
• Review solution designs with architecture, security, and infrastructure teams.
• Maintain architecture diagrams and technical decision records.

15. Testing, Deployment, and Production Support

• Prepare unit, integration, functional, performance, and regression tests.
• Validate end-to-end AI workflows.
• Support user acceptance testing.
• Plan and support application deployments.
• Monitor applications after deployment.
• Investigate incidents and complete root cause analysis.
• Implement permanent fixes to avoid repeated issues.
• Maintain operational runbooks and support documents.
• Provide post-deployment support and knowledge transfer.

16. Documentation and Knowledge Sharing

• Prepare and maintain:
• requirement documents
• solution design documents
• architecture diagrams
• API specifications
• prompt libraries
• test reports
• evaluation reports
• deployment guides
• operational runbooks
• user guides
• Conduct technical knowledge-sharing sessions.
• Support junior team members through reviews and guidance.
• Maintain reusable technical and consulting standards.
• Document assumptions, limitations, and known risks clearly.

Mandatory Skills:

AI and GenAI

• Hands-on experience in building LLM-based applications.
• Strong understanding of AI agents and agent workflows.
• Experience with prompt engineering and tool integration.
• Good knowledge of RAG architecture.
• Understanding of embeddings, chunking, retrieval, and grounding.
• Knowledge of LLM limitations and evaluation methods.
• Experience in implementing AI guardrails.
• Understanding of responsible AI principles.

Programming and Backend Development

• Strong hands-on experience in Python.
• Experience with FastAPI or a similar Python framework.
• Experience with TypeScript or Node.js is preferred.
• Strong API development experience.
• Good knowledge of REST services.
• Working knowledge of GraphQL.
• Ability to write clean, reusable, and maintainable code.
• Strong debugging and troubleshooting skills.

AI Frameworks and SDKs

Hands-on experience with one or more of the following:

• LangChain
• Microsoft Agent Framework
• OpenAI SDK
• Azure AI SDK
• Microsoft Copilot Studio
• equivalent LLM or agent frameworks

Vector Search and RAG Platforms

Experience with one or more of the following:

• Azure AI Search
• Qdrant
• Pinecone
• FAISS
• equivalent vector search platforms

Enterprise Integration

• Experience integrating multiple applications and data sources.
• Knowledge of authentication and authorisation.
• Experience with APIs, databases, and enterprise platforms.
• Ability to convert data and system capabilities into usable AI workflows.
• Understanding of secure enterprise integration patterns.

Cloud and Deployment

• Experience with Azure, AWS, or GCP.
• Hands-on experience with Docker.
• Understanding of container-based deployments.
• Familiarity with Git and CI/CD pipelines.
• Experience working across development and production-like environments.
• Basic understanding of application monitoring and observability.

Good-to-Have Skills

• Microsoft Copilot Studio.
• Azure AI Foundry or Azure AI services.
• Azure OpenAI.
• Claude, Gemini, or open-source LLM integration.
• Multi-agent architecture.
• Model Context Protocol.
• AI workflow orchestration.
• Knowledge graph or GraphRAG exposure.
• SQL and NoSQL database experience.
• SharePoint and Microsoft 365 integration.
• CRM integration experience.
• Azure Functions or AWS Lambda.
• Kubernetes exposure.
• Infrastructure-as-Code knowledge.
• Observability and monitoring tools.
• LLMOps or MLOps experience.
• AI evaluation frameworks.
• Experience in consulting accelerators or internal productivity tools.
• Basic user interface development knowledge.
• Experience with React or another modern frontend framework.

Tools and Technology Stack:

AI and Agent Platforms

• Microsoft Copilot Studio
• Azure AI
• Azure OpenAI
• LangChain
• Microsoft Agent Framework
• OpenAI SDK
• Anthropic Claude
• Google Gemini
• Llama

Programming and APIs

• Python
• FastAPI
• TypeScript
• Node.js
• REST APIs
• GraphQL

RAG and Search

• Azure AI Search
• Qdrant
• Pinecone
• FAISS
• Embedding models
• Hybrid search
• Reranking models

Data and Enterprise Platforms

• SharePoint
• CRM platforms
• SQL databases
• NoSQL databases
• Enterprise APIs
• Cloud storage

DevOps and Deployment

• Git
• GitHub
• Azure DevOps
• GitHub Actions
• Docker
• Container registries
• Azure, AWS, or GCP

Experience Requirements:

• 4–8 years of relevant professional experience.
• Experience in software engineering, AI/ML engineering, data engineering, or automation roles.
• Proven experience in building scalable applications or enterprise tools.
• Hands-on experience with LLM or AI-enabled systems.
• Experience integrating multiple enterprise systems.
• Experience with production or near-production AI solutions.
• Experience in rapid prototyping and iterative development.
• Experience working with both technical and non-technical stakeholders.

A background in consulting, product development, or internal tooling will be an advantage.

Educational Qualification:

Bachelor’s or Master’s degree in:

• Computer Science
• Information Technology
• Artificial Intelligence
• Data Science
• Software Engineering
• Information Systems
• or a related discipline

Strong practical experience can also be considered.

Soft Skills:

• Strong ownership and accountability.
• Builder and problem-solving mindset.
• Ability to convert ideas into working solutions quickly.
• Comfortable working with changing or unclear requirements.
• Ability to balance delivery speed with engineering quality.
• Clear communication with technical and non-technical stakeholders.
• Strong collaboration and consulting skills.
• Good documentation and presentation skills.
• Willingness to experiment, learn, and improve.
• Ability to work independently and within a cross-functional team.
• Customer-focused and business-value-oriented approach.
• Ability to raise risks and dependencies early.

Preferred Candidate Profile:

The preferred candidate should have:

• Strong hands-on AI application development experience.
• Experience building AI agents, copilots, or productivity tools.
• Ability to own a solution from initial idea to deployment and support.
• Strong backend and API development skills.
• Practical experience with RAG and enterprise data integration.
• Experience working directly with business or consulting teams.
• Ability to build rapid prototypes without compromising basic quality and security.
• Strong understanding of AI limitations, governance, and responsible use.
• Interest in applying AI to real business and consulting challenges.
• Ability to work effectively in a fast-moving and experimental environment.
• Ability to balance technical design with business outcomes.