Here is an exciting Opportunity - AI Engineer || Capgemini

Key Responsibilities

• Design, build, and evolve AI agents for software architecture, code review, testing, modernization, documentation generation, and release management.
• Define and implement skills, prompts, templates, orchestration patterns, and reusable AI capabilities across the platform.
• Build multi-step workflows that coordinate agents, tools, human approvals, governance policies, and external systems.
• Improve reasoning quality, context management, retrieval mechanisms, and response accuracy across AI-powered workflows.
• Evaluate and integrate leading AI technologies including GitHub Copilot, Claude Code, OpenAI models, MCP servers, and emerging agentic frameworks.
• Develop enterprise-grade prompt engineering, context engineering, and grounding strategies for software engineering use cases.
• Build integrations between AI agents and developer ecosystems including GitHub Enterprise, Azure DevOps, Jira, knowledge repositories, and developer platforms.
• Design governance-aware execution models that enforce organizational policies, security controls, auditability requirements, and approval gates.
• Continuously benchmark the tool/platform capabilities and identify improvement opportunities.
• Drive innovation around autonomous software development, Spec Driven Development, AI-assisted modernization, and engineering productivity acceleration.

Mandatory Skills

AI Engineering: Hands-on experience working with LLMs, AI agents, RAG architectures, prompt engineering, context engineering, tool calling, MCP, memory systems, and agent orchestration.

Agentic Frameworks: Experience building or customizing agent-based systems, autonomous workflows, multi-agent solutions, or AI copilots.

TypeScript Ecosystem: Strong development skills using TypeScript, Node.js, npm, modern JavaScript frameworks, and API integrations.

VS Code Extension Knowledge: Working knowledge of VS Code extensions, Chat Participants, Commands, WebViews, extension APIs, async programming, promise handling and developer tooling ecosystems

AI Developer Tools: Practical experience using GitHub Copilot, Claude Code, Cursor, Windsurf, Continue.dev, or similar AI-assisted engineering platforms.

Software Engineering: Strong understanding of software architecture, design patterns, code quality, testing, DevOps, CI/CD, secure SDLC, and modernization initiatives.

Integration Development: Experience integrating applications with GitHub, Azure DevOps, Jira, REST APIs, MCP Servers, databases, and enterprise platforms.

Problem Solving: Ability to translate complex SDLC challenges into reusable workflows, agents, AI capabilities, and platform services.

Testing Skills: Familiarity with unit testing frameworks like Jest, Mocha, etc.