AI Platform Architect - AI SDLC Platform on Microsoft Azure.

CNX•Omaha, NE

1d•$122,001 - $150,000•Onsite

About The Position

We are looking for an experienced AI Platform Architect – AI SDLC Platform on Microsoft Azure to lead the day-to-day technical architecture and implementation design of an enterprise AI SDLC platform. In this onsite role based in Omaha, Nebraska, you will partner with the Chief AI SDLC Platform Architect to translate strategic architecture into practical, scalable platform designs that engineering teams can successfully implement. This is an opportunity to shape reusable AI-enabled engineering capabilities across developer workflows, agentic systems, context services, evaluation, observability, governance, and application modernization—especially within complex enterprise, manufacturing, industrial, or engineering environments.

Requirements

8+ years of software engineering experience, including meaningful architecture responsibility.
2+ years of hands-on experience with Generative AI, AI-assisted engineering, or agentic solutions.
Strong experience in platform, application, or solution architecture, including APIs, distributed systems, integration, scalability, resilience, and security.
Hands-on experience with Microsoft Azure, including Azure AI/OpenAI, Azure DevOps, Entra ID, Azure application hosting, storage, monitoring, and security services.
Practical experience with Generative AI, LLMs, coding agents, agent orchestration, MCP, tool calling, and multi-step AI workflows.
Experience using GitHub Copilot, Claude Code, or equivalent AI developer tools.
Knowledge of context engineering, RAG, knowledge retrieval, embeddings, and AI/LLM frameworks such as LangGraph, Semantic Kernel, LangChain, or equivalent.
Strong understanding of CI/CD, DevOps, software testing, code review, containers, and production engineering practices.
Working proficiency in Python.
Strong experience with .NET / C# enterprise application architecture and development.
Experience with AI evaluation, observability, security, governance, reliability, and cost management.
Demonstrated ability to move architecture from design through successful production implementation.
Strong technical leadership, communication, and collaboration skills.

Nice To Haves

Experience in manufacturing, industrial, or engineering environments.
Familiarity with CAD/engineering drawings, PLM/PDM, MES, ERP, BOMs, or engineering change processes.
Experience with brownfield .NET application modernization.
Experience with Kubernetes, Terraform, OpenTelemetry, or self-hosted models.

Responsibilities

Own the detailed technical architecture and implementation design for the enterprise AI SDLC Platform on Microsoft Azure.
Translate strategic architecture direction into practical, scalable, secure, and production-ready platform solutions.
Design reusable AI platform capabilities, including coding agents, skills, context services, MCP/tool integrations, model access, workflow orchestration, evaluation, observability, security, governance, and traceability.
Define how AI is applied across the software development lifecycle, including requirements, architecture, development, code review, testing, CI/CD, modernization, and operations.
Architect integrations with Azure DevOps, Git, IDEs, CI/CD pipelines, enterprise APIs, security and code-quality tools, and enterprise knowledge sources.
Establish technical patterns for AI-assisted application modernization, including code understanding, refactoring, test generation, framework upgrades, and migration.
Partner closely with engineering teams to guide implementation, solve complex technical challenges, and promote consistent platform adoption.
Lead technical design sessions and create architecture guidance, implementation patterns, and decision frameworks.
Support platform capabilities that enable secure, observable, reliable, cost-conscious, and governed AI-assisted engineering workflows.
Collaborate across technical and business stakeholders to ensure platform designs align with enterprise engineering needs and delivery outcomes.