About the role

We are looking for a senior, hands-on AI engineering leader to design, build and deploy enterprise AI applications, intelligent automation solutions and production-grade AI agents on Microsoft Azure. You will combine strong Python/software engineering with Microsoft Agent Framework (MAF), Claude and other open-source agent technologies, along with deep Power Apps and Power Automate implementation skills. You will lead a small engineering team, remain directly involved in code and solution design, and take solutions from discovery through production and support.

What you will do

Own solution design and hands-on delivery of AI-enabled business applications, multi-agent workflows and enterprise automation programs; convert business processes into secure, scalable technical designs.

Build and orchestrate AI agents using Microsoft Agent Framework (MAF), Python, Microsoft Foundry/Azure OpenAI, Anthropic Claude and suitable open-source frameworks; implement tool calling, MCP integrations, state management, RAG and human-in-the-loop controls.

Develop Power Apps (canvas and/or model-driven), Power Automate cloud flows, Dataverse-backed applications, approval journeys, exception queues, notifications, audit trails and reusable portal/landing-zone components.

Engineer solutions for document and data extraction, forecast/presentation preparation, invoice and payment processing, asset and fleet onboarding, incentive/commission processing and cross-functional workflow tracking.

Integrate solutions with enterprise systems such as ERP, finance, HR, operations and legacy/on-premise applications using REST APIs, custom connectors, Azure Functions, Logic Apps, API Management, SQL and secure identity patterns.

Build reliable cloud services and data/AI pipelines; apply CI/CD, automated testing, logging, observability, evaluation, cost controls, access management and production troubleshooting.

Lead and mentor engineers, review code and architecture, split work into deliverable increments, manage technical risks, collaborate with customer stakeholders and own quality through go-live.

Identify opportunities for future predictive AI/ML, document intelligence, knowledge assistants, decision support and process optimization; prototype and productize the strongest use cases.

Required technical experience

10–12+ years in software/cloud engineering, including substantial hands-on Azure development and 3+ years of technical leadership or ownership of end-to-end delivery.

Expert Python development (APIs, FastAPI or equivalent, SDKs, integrations, testing, background processing and production debugging); solid SQL, data modeling and software engineering fundamentals.

Hands-on enterprise experience with Power Apps, Power Automate, Dataverse, SharePoint/Microsoft 365 integrations, custom connectors, approvals, environment management, security and ALM.

Practical GenAI/agent engineering: LLM APIs, prompt and context engineering, tool/function calling, RAG, embeddings/vector search, agent orchestration, structured outputs, evaluations and safeguards.

Ability to build with Microsoft Agent Framework or demonstrate equivalent production multi-agent/workflow engineering using frameworks such as LangGraph, Semantic Kernel or AutoGen, with willingness to work directly in MAF.

Experience with Claude models and APIs; ability to build repeatable workflows using Claude Code/Claude Skills or comparable agent tooling.

Azure services such as Microsoft Foundry/Azure AI, Azure AI Search, Functions, Logic Apps, Storage, Key Vault, Entra ID, API Management, Application Insights and Azure DevOps/GitHub CI/CD.

Demonstrated expertise in REST/API integration, RBAC, secrets, environment separation, failure handling, monitoring, approval gates and enterprise-grade auditability.

Good to have

Experience with ML use cases including forecasting, anomaly detection, classification, recommendation or optimization using scikit-learn, PyTorch or other appropriate tools.

Familiarity with Microsoft Fabric, Power BI, Azure Data Lake, data pipelines, Document Intelligence, Copilot Studio and AI Builder.

Experience modernizing finance/operations workflows connected to NetSuite, Oracle, SAP or other enterprise applications.

Experience deploying containerized services or working with Kubernetes, infrastructure as code and cloud architecture best practices.

What success looks like

Working AI/automation solutions delivered in short increments, with measurable business value and reliable production operations.

Reusable engineering patterns for agents, applications, connectors, approval workflows, security, evaluation and observability.

An effective engineering team that produces high-quality code and can support both current automation programs and emerging AI/ML initiatives.

Who will thrive here

You are a builder and technical leader—not only a low-code developer, prompt engineer or high-level architect. You are comfortable writing code, designing systems, coaching engineers and presenting your solution choices to business and technology stakeholders. You select deterministic automation when it is the safer choice and AI agents when reasoning, unstructured data or dynamic decision-making add clear value.