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Senior AI Agent Engineer

Encora

Nuevo Remote
Remote Senior 🇬🇧 English
Generative AI Azure AI Foundry Azure OpenAI Copilot Studio Python Semantic Kernel MCP RAG Prompt Engineering Agent Evaluation Guardrails

Descripcion del puesto

About the role

Coforge is hiring a Senior AI Agent Engineer to design, develop, test and deploy enterprise AI agents, copilots and multi-agent systems. The position is remote and open to candidates in Costa Rica, Peru, Colombia and Bolivia.

Key responsibilities

  • Design, develop, test and deploy enterprise AI agents, copilots and multi-agent systems using Azure AI Foundry, Copilot Studio, Python-based frameworks and other approved technologies.
  • Develop prompts, instructions, tool definitions, routing and orchestration workflows, with grounding, citations, retrieval, context management, guardrails and human-review patterns.
  • Integrate AI agents with enterprise tools and APIs, build rapid prototypes and validate solutions with business users.
  • Create automated evaluations and continuously optimise agent quality, latency, reliability, observability and cost, supporting production monitoring and troubleshooting.

Required profile

  • 5+ years of software development experience, including 2+ years building Generative AI, AI assistant or agent-based applications.
  • Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence or a related field.
  • Strong hands-on experience with Generative AI (mandatory).

Required skills

  • Azure AI Foundry, Azure OpenAI and Copilot Studio.
  • Strong Python and REST API integration.
  • Semantic Kernel, MCP and agent orchestration.
  • Prompt engineering and RAG.
  • Agent evaluation, guardrails, grounding, citations and context management.
  • Observability, monitoring and continuous improvement for production AI apps.
  • Experience deploying secure, testable enterprise applications.

Preferred: advanced experience with Azure AI Foundry and Copilot Studio, Azure AI Engineer certification, and experience with RAG, automated evaluation pipelines and tool-use controls.

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