An Artificial Intelligence Engineer develops, tests, and deploys artificial intelligence models. May work closely with data software engineers and data professionals to train and implement AI models into existing systems or develop new applications.
Design, develop, deploy, and operate AI-powered applications, agents, and automation workflows, using LLMs, foundation-model APIs, retrieval, embeddings, and structured or deterministic software as appropriate to the problem. Build secure, reliable, observable, and testable production systems - not prototype-only or notebook-only solutions. Design APIs, integrations, distributed services, asynchronous processes, and workflow orchestration that connect AI capabilities to enterprise systems. Develop evaluation, benchmarking, monitoring, and observability capabilities for AI and agentic systems, and investigate issues through systematic root-cause analysis. Identify manual, inefficient, or fragmented workflows and replace them with reusable, auditable software and automation capabilities that can serve multiple use cases. Apply modern engineering practices - source control, automated testing, CI/CD, infrastructure as code, containers, and production monitoring - across everything you ship. Partner directly with business and technology stakeholders to translate ambiguous problems into practical, measurable solutions, and communicate technical decisions clearly to technical and non-technical audiences. Required Qualifications Strong professional software engineering experience with ownership of production applications or systems. 10 years of experience Strong hands-on programming experience in Python and/or Go . Hands-on experience building applications using LLMs, foundation-model APIs, agent frameworks, or related AI technologies. Experience designing production APIs, services, integrations, asynchronous workflows, or distributed applications. Experience with cloud-native application development, preferably in Azure or a comparable enterprise cloud environment. Working knowledge of CI/CD, automated testing, containers, infrastructure as code, monitoring, and observability. Experience troubleshooting production systems and supporting applications after deployment. Ability to operate effectively with incomplete requirements and limited day-to-day technical direction. Ability to make sound architectural and engineering tradeoffs and explain those decisions clearly. Strong communication skills across engineering, architecture, business, and product stakeholders. Preferred Qualifications Production experience building AI agents or agentic workflows. Experience with ADK, LangGraph, or comparable agent orchestration technologies. Experience with Azure AI Foundry and Azure AI Search. Experience with retrieval-augmented generation, enterprise search, embeddings, and knowledge integrations. Experience with AI evaluation and testing across retrieval, reasoning, tool calling, structured output, and model behavior. Experience designing secure AI systems using identity, authorization, guardrails, auditing, and controlled tool access. Experience with Terraform and AKS or comparable infrastructure/container platforms. Experience integrating AI systems into enterprise APIs, workflows, and applications. Experience with workflow automation platforms such as UiPath. Healthcare, payer, claims, customer-service, or other regulated-industry experience. Experience guiding technical decisions, establishing engineering patterns, or mentoring other engineers.
