Senior AI Engineer / Technical Lead (Agentic AI & MCP)

We are looking for a hands-on Senior AI Engineer to lead the technical design and delivery of enterprise AI agents and MCP-based integrations.

The team will develop Model Context Protocol (MCP) services for local business applications and build AI agents that consume both these local MCPs and MCP services provided by a global platform team.

The Senior AI Engineer will own solution design, support use-case discovery, provide technical leadership to the engineering team, and participate directly in implementation.

• Experience building LLM-based applications, AI agents or agentic workflows .
• Strong knowledge of REST APIs and enterprise integrations.
• Experience with authentication/authorization such as OAuth 2.0 and Microsoft Entra ID.
• Understanding of tool/function calling and agent‑to‑system integration patterns.
• SQL, JSON, Git and CI/CD.
• Ability to design production‑grade solutions and provide technical leadership.

• Power Automate.
• Agent orchestration frameworks such as Semantic Kernel, LangGraph or similar.
• Azure Functions / API Management / Logic Apps.
• Experience with enterprise AI governance, evaluation and observability.

• Lead technical discovery and translate business use cases into AI/agentic solution designs.
• Design the architecture of AI agents and their interaction with local and global MCP services.
• Design and develop MCP services exposing capabilities of local applications to AI agents.
• Build enterprise AI agents and agentic workflows.
• Integrate agents with multiple MCP servers, APIs and enterprise systems.
• Define agent instructions, reasoning flows, tool‑selection logic, approval workflows and human‑in‑the‑loop guardrails.
• Define reusable patterns and engineering standards for MCP and agent development.
• Lead and support other AI engineers during implementation.
• Ensure compliance with enterprise security, architecture and AI governance requirements.
• Define and monitor agent quality and performance metrics, including task completion, tool failures, hallucinations, latency and cost.
• Collaborate with global AI/platform teams to ensure interoperability between local and global MCP capabilities.