Agentic AI Platform Engineer

3Core Systems

  • Denver, Colorado
  • 2 days ago

    Highlights

    The ideal candidate will have a strong full-stack software engineering background, hands-on experience building AI/agentic applications, and practical knowledge of Microsoft Copilot Studio, Power Platform, APIs, cloud development, identity/security, telemetry, automated testing, and AI tool orchestration. This engineer will work closely with platform and architecture leadership to implement technical controls and reusable capabilities that enable AI agents to operate safely and reliably within a global enterprise environment.

    Numbers & Facts

    LocationDenver, Colorado

    Description

    Role: Agentic AI Platform Engineer
    Location:
    Denver, CO – Hybrid (2–3 days onsite per week)
    Duration:
    6–12 Month Contract-to-Hire
    Work Authorization: US Citizen or Green Card Holder

     

    Position Overview

    Client is seeking a hands-on Agentic AI Platform Engineer to help transform AI proofs of concept into secure, governed, scalable, and production-ready enterprise capabilities.

    This is an engineering-focused role for someone who can move beyond AI strategy and experimentation and actually build, integrate, test, secure, monitor, and operate AI-powered applications and agentic platforms.

    The ideal candidate will have a strong full-stack software engineering background, hands-on experience building AI/agentic applications, and practical knowledge of Microsoft Copilot Studio, Power Platform, APIs, cloud development, identity/security, telemetry, automated testing, and AI tool orchestration.

    This engineer will work closely with platform and architecture leadership to implement technical controls and reusable capabilities that enable AI agents to operate safely and reliably within a global enterprise environment.

     

    Required Skills:

    • Hands-on Agentic AI / AI application engineering
    • Full-stack software development background
    • Microsoft Copilot Studio
    • Low-code / Power Platform
    • APIs, integrations and connectors
    • AI agents/tool orchestration
    • Identity/security around AI agents
    • Telemetry/observability
    • Automated testing/evaluation of AI output
    • Cloud development
    • Productionizing AI POCs
    • SAP and ServiceNow AI/agent exposure as a plus

     

    Key Responsibilities

    • Design, develop, and productionize AI applications, AI agents, and agentic workflows.
    • Transform AI proofs of concept into secure, scalable, reliable, and maintainable production solutions.
    • Build reusable components and platform capabilities for enterprise AI and agentic applications.
    • Develop integrations using REST APIs, SDKs, connectors, webhooks, and enterprise services.
    • Implement AI agent tool orchestration, tool access controls, and integration patterns.
    • Develop and implement sandboxing and isolation mechanisms for AI agents and applications.
    • Implement secure identity, authentication, authorization, and access-control patterns for AI workloads and agents.
    • Build telemetry, logging, monitoring, tracing, and observability capabilities for AI applications and agents.
    • Develop automated testing and evaluation frameworks to assess AI output quality, reliability, safety, and performance.
    • Implement state management, recovery, error handling, and resilience patterns for agentic workflows.
    • Build and maintain cloud-based AI application components and supporting infrastructure.
    • Work with Microsoft Copilot Studio and Power Platform to build and integrate enterprise AI capabilities.
    • Develop low-code and pro-code solutions where appropriate, understanding how to integrate both approaches effectively.
    • Establish reusable implementation patterns for AI agents, tools, integrations, and platform services.
    • Collaborate with architecture, security, governance, data, and application teams to ensure solutions meet enterprise standards.
    • Troubleshoot and optimize AI applications across development, testing, and production environments.
    • Use command-line tools, IDEs, source control, CI/CD pipelines, and cloud development environments as part of day-to-day engineering activities.
    • Help establish engineering standards and technical controls that enable responsible and scalable adoption of enterprise AI.


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