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

Intellias; España; On-site; EUR 70,000 - 110,000; Full time

Job summary

Intellias is seeking a hands-on Senior AI Engineer in Spain to lead the technical design and delivery of enterprise AI agents and MCP-based integrations. You will own solution design, support use-case discovery, and provide technical leadership while actively contributing to implementation.

You'll architect interactions between local MCP services and global MCP capabilities, define reusable patterns, and ensure governance and security across the AI stack.

Qualifications

• 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.

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.

Skills

LLM-based apps

AI agents

REST APIs

OAuth 2.0

Agent-to-system integration

SQL

JSON

Git

CI/CD

Production-grade design

Tools

Power Automate

Semantic Kernel

LangGraph

Azure Functions

Job description

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.

Required skills

• 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.

Strong advantage

• 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.