AI Engineer Role

AI Engineers build and operate systems that turn data, documents, models, and AI services into useful applications and workflows. The work runs from understanding the problem and preparing inputs through model or service selection, system design, evaluation, deployment, monitoring, incident response, and continuous improvement.

This is systems engineering around AI behavior. AI Engineers connect data pipelines, models, APIs, user experiences, business rules, and human-review paths, then test the complete system under conditions that reflect how people will use it. They make limitations visible and match security, traceability, and oversight to the consequences of the work.

What you’ll build

• Document and content pipelines that apply OCR, parse layouts, extract and validate information, preserve provenance, and make authoritative content usable downstream.
• Predictive, classification, recommendation, natural language, computer vision, or decision-support services that combine custom models, pretrained models, externally provided AI services, and deterministic software.
• Search and knowledge applications that use retrieval, retrieval-augmented generation (RAG), controlled source access, and traceable references.
• Workflow and agent-based systems with scoped tools, permission boundaries, approval paths, exception handling, and audit trails where agentic patterns fit the problem.
• Evaluation and operations capabilities that version, test, release, trace, monitor, diagnose, and roll back models, prompts, configurations, retrieval indexes, and supporting software.

Who you are

You see the model as one part of a larger system. You can move between user needs, data, software, model behavior, and production operations without losing sight of the outcome, and you choose the simplest approach that meets the need.

You test assumptions against evidence, communicate tradeoffs clearly, and work comfortably with domain experts, users, data engineers, data scientists, Machine Learning Engineers, security specialists, governance teams, and operations teams. You know when to build, when to integrate, when to escalate, and when AI is not the right answer., * A working foundation in software engineering, including programming, APIs, testing, version control, and the design of services or applications.

• Practical experience with structured or unstructured data, including preparation, validation, metadata, access controls, quality, and traceability.
• Applied understanding of AI and machine learning, including model or service selection, evaluation design, error analysis, and clear communication of limitations.
• Experience contributing to production reliability through deployment discipline, observability, performance and cost management, security, incident response, or related operational practices.
• The judgment to match evaluation, documentation, safeguards, and human oversight to the context and consequences of the system.