RAG Pipeline, AgenticAI, Python, Azure The platform should enable teams to build, deploy, and operate AI agents using a variety of development approaches and frameworks while providing a standardized enterprise foundation for orchestration, integration, governance, security, and observability.
The engineer should have experience designing agentic workflows and multi-agent orchestration, including both event-driven and workflow-based patterns. The platform should support scalable communication and coordination between agents, enterprise systems, tools, APIs, and diverse data sources.
A key responsibility will be establishing enterprise-grade observability across the platform, including centralized instrumentation, tracing, operational metrics, performance monitoring, error tracking, and visibility into agent execution and behavior.
The platform should also provide a centralized gateway for AI model interactions, agent tools, and external services, ensuring consistent security, governance, authentication and authorization, access control, throttling, monitoring, and policy enforcement.
The ideal engineer will have experience building scalable, extensible, and technology-agnostic AI platforms that allow different teams and business domains to develop agents independently while adhering to common enterprise standards for interoperability, security, governance, reliability, and operational management.
The role should also focus on building production-grade AI engineering capabilities, including Agent Harness Engineering, automated and closed-loop evaluation, feedback loops, prompt and model evaluation, observability, guardrails, resiliency, and continuous improvement mechanisms.
The goal is to create a reusable platform where multiple business domains can build, deploy, monitor, and operate autonomous agents using standardized enterprise patterns rather than creating isolated agent solutions.
Proven experience architecting and delivering systems using agentic IDEs Ability to:
• Define architectural intent that agents can follow
• Break features into agent executable tasks
• Govern AI autonomy (guardrails, permissions, reviews)
• Integrate agentic workflows into CI/CD pipelines Experience supervising AI agents across:
• Multi service systems
• Legacy modernization
• Large codebases / monorepos Strong understanding of:
• Security implications of autonomous code execution
• Compliance, auditability, and traceability
• AI assisted SDLC operating models Core Responsibility:
• Guide effective use of agentic IDEs for complex, multi-module or cross-service changes
• Establish review practices and quality checks for AI-generated code
• Mentor team members on balancing autonomy, correctness, and maintainability in AI-assisted development
• Design system architectures that support AI-augmented and agentic development workflows
• Define guardrails, standards, and governance for the use of autonomous coding agents
• Evaluate impact of agentic IDEs on SDLC, CI/CD pipelines, security posture, and technical debt
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