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AI Engineer Job Description (2026): Duties, Skills and Salary

An AI engineer builds products on top of machine learning models, especially large language models: designing prompts and retrieval pipelines, fine-tuning or evaluating models, and wiring the result into an application with proper guardrails, monitoring and fallback behavior. Unlike a research-focused data scientist, the AI engineer's main output is shipped features, not papers or notebooks.

The role sits close to backend engineering, since most of the work is API integration, pipeline design and evaluation harnesses rather than training models from scratch. Common backgrounds include software engineering with a machine learning focus, or data science roles that moved toward production systems.

AI Engineer job description template

Copy it, replace everything in [brackets] and cut what doesn't apply. Keep the requirements to what the job really needs.

Template · replace the [brackets]

AI Engineer

[Company] · [City and office days, or remote with the countries or time zones you hire in] · [Full-time]

About [Company]

[Two or three sentences on what your company does, who it serves and why this team matters now.]

About the role

[Company] is hiring an AI engineer to build [feature, e.g. an AI assistant or document search] using [LLM provider/stack]. You'll design the pipeline from prompt or retrieval to response, evaluate quality, and ship it into [product].

What you'll do

  • Design and build LLM-powered features, including prompting, retrieval and tool-use pipelines.
  • Evaluate model outputs for quality, safety and cost, and build automated eval suites.
  • Fine-tune or adapt models when prompting alone isn't sufficient.
  • Integrate models into production services with proper monitoring and fallback handling.
  • Manage vector databases and retrieval pipelines for grounding model outputs in real data.
  • Optimize for latency and cost across model choice, caching and batching.
  • Work with product to scope what AI can realistically do well in a given feature.
  • Stay current on model releases and evaluate whether to adopt them.

What you'll bring

  • [X]+ years in software engineering, with [Y]+ building production ML or LLM features.
  • Experience with LLM APIs (OpenAI, Anthropic, or similar) and prompt design.
  • Comfortable building retrieval-augmented generation (RAG) pipelines and working with vector databases.
  • Strong Python skills, including familiarity with ML/LLM frameworks such as LangChain or LlamaIndex.
  • Experience building evaluation pipelines to measure model quality objectively.
  • Understanding of the cost, latency and reliability tradeoffs of different model choices.

Nice to have

  • Experience fine-tuning open-source models.
  • Background in traditional machine learning or data science.
  • Experience with AI safety, guardrails or content moderation systems.
  • Familiarity with MLOps tooling for deployment and monitoring.

What we offer

  • [Pay range: e.g. $X–$Y base salary, plus bonus or equity if offered]
  • [GPU/compute budget, conference travel, flexible hours]
  • [Health cover, retirement or pension plan, paid time off, parental leave]
  • [Work authorization: whether you can sponsor a visa or hire through an employer of record]

[Our process: a recruiter call, a technical interview on past AI/ML projects, a take-home or live exercise building a small pipeline, and a final conversation with the team.]

[Your equal opportunity statement, and how candidates can ask for adjustments during the process]

Put the pay range in the ad. Several US states, including California, Colorado, New York and Washington, require it in job postings, and the EU Pay Transparency Directive gives applicants the right to know the starting pay or its range before the interview.

AI Engineer duties by seniority

What the job usually looks like at each level. Pitch the ad at one level and say which in the title.

Junior

  • Builds and iterates on prompts and pipelines under guidance.
  • Runs evaluations and reports on model quality for a defined feature.
  • Learns the tradeoffs between different models and retrieval strategies.

Mid-level

  • Owns an AI feature end to end, from pipeline design to production monitoring.
  • Builds evaluation suites and decides when to fine-tune versus prompt.
  • Balances cost, latency and quality for production use cases.

Senior

  • Sets AI architecture and model strategy across multiple features.
  • Leads evaluation and safety standards for AI features company-wide.
  • Mentors other engineers and advises leadership on model and vendor choices.

AI Engineer career path

  1. Software Engineer
  2. AI Engineer
  3. Senior AI Engineer
  4. AI/ML Lead or Head of AI

AI engineer is a relatively new title, usually reached from backend software engineering with a growing focus on ML systems, or from data science roles that moved toward production work. From here, people move into senior AI engineering, ML leadership, or applied research roles.

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AI Engineer job description FAQ

What does an AI engineer do?

Builds product features on top of machine learning models, especially LLMs: designing prompts and retrieval pipelines, evaluating quality, and integrating models into production with monitoring and fallbacks.

What skills should an AI engineer job description require?

Strong software engineering fundamentals, hands-on experience with LLM APIs and retrieval-augmented generation, and the ability to build evaluation pipelines. Deep ML theory is less important than for a machine learning engineer role.

How is an AI engineer different from a machine learning engineer?

AI engineers typically integrate and adapt existing models (often LLMs) into products, while machine learning engineers more often train and deploy custom models. The line varies by company, so describe the actual day-to-day work.