About the AI Engineer role
An AI Engineer designs, builds, and deploys artificial intelligence systems: machine learning models, large language model applications, and the data pipelines that feed them. Unlike research-focused roles, AI engineers are measured on working software. They turn models into reliable products that run in production and improve with real usage.
The role sits between data science and software engineering. A strong AI engineer writes production-quality code, understands model behavior well enough to debug it, and knows how to evaluate whether a system is actually good before shipping it. In many teams, they also integrate third-party foundation models and build the guardrails, evaluation suites, and monitoring around them.
Be explicit in your posting about the problems the engineer will work on, your ML stack, and whether the role leans toward training models, building LLM-powered features, or MLOps infrastructure. The AI field is broad, and precise scope attracts precisely matched candidates.
AI Engineer job description template
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Job brief
Join us as an AI Engineer and take our models from something that works in a notebook to something people can rely on. You will build and deploy the systems, wire them into products, evaluate whether they are genuinely good before they ship, and keep watching them once real usage arrives.
This suits an engineer first: somebody who writes production code, debugs model behavior rather than shrugging at it, and can explain a trade-off to a colleague who does not want the mathematics.
Responsibilities
- Design, build, and deploy machine learning models and AI-powered features into production
- Develop and maintain data pipelines for training, evaluation, and inference
- Integrate and fine-tune large language models and other foundation models where appropriate
- Build evaluation frameworks to measure model quality, safety, and regression before release
- Monitor production AI systems and improve their accuracy, latency, and cost over time
- Collaborate with product managers and designers to scope AI features that solve user problems
- Write clean, tested, production-quality code and participate in code reviews
- Stay current with AI research and tooling, and evaluate new techniques for practical use
- Document systems, experiments, and decisions so the team can build on your work
Requirements and skills
- Has shipped machine learning or artificial intelligence systems into production
- Strong in Python, with the software engineering habits that go with it
- Hands-on with a framework such as PyTorch or TensorFlow
- Has deployed and operated models behind APIs or batch pipelines
- Solid on data structures, data modeling, and SQL
- Familiar with the patterns around large language models: retrieval, prompting, fine-tuning
- Explains a technical trade-off to somebody without the background
- A relevant degree, or the practical experience that replaced one
Nice to have
- Experience with cloud ML platforms such as AWS SageMaker, Google Vertex AI, or Azure ML
- Experience with MLOps tooling for experiment tracking, model registries, and CI/CD
- Contributions to open-source ML projects or published applied research
- Experience with vector databases, embeddings, and semantic search
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