Senior AI Engineer

Intellias helps retailers deliver consistent, omnichannel experiences through technology. We are looking for a hands-on Senior AI Engineer to lead the technical design and delivery of enterprise AI agents and Model Context Protocol (MCP)-based integrations, building scalable solutions that drive performance and customer value.The team will develop MCP services for local business applications and AI agents that consume both these local MCPs and 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 stay directly involved in implementation.

Requirements

Strong hands-on Python engineering experience.Experience building LLM-based applications, AI agents or agentic workflows.Strong knowledge of REST APIs and enterprise integrations.Strong Microsoft Azure experience.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.Strong English communication skills.Nice to haveHands-on Model Context Protocol (MCP) experience.Azure AI Foundry / Azure OpenAI.Copilot Studio.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.

Responsibilities

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.