AI engineer job description template (2026)
A complete AI engineer job description you can copy, plus seniority levels and tips for hiring someone who ships reliable LLM features to real users.
By the Ryz Labs team · Updated October 2026
An AI engineer job description should say which AI features the person will ship, to whom, and how you will know they work. In 2026 "AI engineer" usually means a software engineer who builds product features on top of foundation models: retrieval over your own data, tool-calling agents, structured extraction, copilots inside existing workflows. That is different from training models or running GPU clusters. Name the model providers you use, the data the features touch, and how quality is measured. The template below is written for a senior AI engineer who ships LLM-powered features into a production product and owns their evaluation, cost and reliability.
AI engineer job description template
Job title
Senior AI Engineer (LLM Product Features)
Employment type: full-time or contract. Location: remote, with at least four hours of overlap with US Eastern time.
About the role
We are looking for a senior AI engineer to build the AI features in [product name]. Users rely on us for [workflow], and we are adding [assistants / document extraction / search / agents] that use models from [Anthropic / OpenAI / open-weight models] over our own data in [Postgres / a vector database / object storage]. You will own features from prototype to production: retrieval, prompts, tool use, evaluation, guardrails, cost and the user experience around them. You will work with product, design and backend engineers and report to [title].
Responsibilities
- Design and ship LLM-powered features end to end, from API and data access to the UI states users see while a model is working or fails.
- Build retrieval-augmented generation pipelines: document parsing, chunking, embeddings, hybrid search, reranking and citation of sources.
- Build agents and tool-calling workflows with clear tool schemas, step limits, timeouts and safe handling of side effects.
- Use structured outputs and schema validation so model responses can be trusted by downstream code.
- Create evaluation sets from real user tasks, and run automated evals in CI with code-based checks and calibrated LLM-as-judge graders.
- Instrument features with tracing for prompts, retrieved context, tool calls, latency, tokens and cost, using tools such as Langfuse, LangSmith or OpenTelemetry.
- Manage cost and latency with prompt caching, model routing, streaming responses and batching where it fits.
- Add guardrails against prompt injection, data leakage between tenants and unsafe outputs, and respect access permissions in retrieval.
- Run online experiments and collect user feedback to decide whether a change actually improved the feature.
- Handle provider issues: rate limits, outages, model version changes and fallbacks between providers.
- Share patterns and reusable components so other product engineers can build AI features safely.
Requirements
- 5+ years of software engineering, with at least 1-2 years shipping LLM-powered features to production users.
- Strong backend skills in Python or TypeScript, including APIs, async code, queues and testing.
- Hands-on experience with major model APIs, including tool use, structured outputs, streaming and prompt caching.
- Production experience building retrieval over real company data, with a clear view of chunking, embedding and reranking trade-offs.
- Experience designing evaluation sets and using them to decide between prompts, models and retrieval changes.
- Understanding of LLM failure modes: hallucination, prompt injection, context overflow, non-determinism and silent quality regressions after model updates.
- Experience with a vector store such as pgvector, OpenSearch, Pinecone, Weaviate or Qdrant.
- Product sense: you can tell when a deterministic rule beats a model call, and you design for the cases where the model is wrong.
- Clear written English for design docs, eval reports and product discussions.
Nice to have
- Experience with the Model Context Protocol (MCP) for connecting models to tools and data.
- Frontend experience with React or Next.js and streaming UIs.
- Agent frameworks such as LangGraph, the OpenAI Agents SDK or the Claude Agent SDK.
- Voice or multimodal features, such as speech-to-text, text-to-speech or document vision.
- Experience in regulated domains where outputs need review and audit trails.
- Cloud model platforms such as Amazon Bedrock or Azure OpenAI.
Tech stack
Python 3.12, FastAPI, TypeScript, Next.js, Anthropic and OpenAI APIs, Postgres with pgvector, Redis, Langfuse, Promptfoo, AWS, GitHub Actions. Replace this with your real stack and model providers; AI engineers check this before anything else.
What success looks like in 6 months
- You have shipped at least one AI feature that real users rely on, with usage and quality numbers from our own product.
- Every AI feature has an eval set that runs in CI, and prompt or model changes are judged against it before release.
- Cost and latency per request are tracked and within an agreed budget.
- Production traces let anyone on the team see why the model gave a specific answer.
How to apply and interview process
Send your resume or LinkedIn profile and a short note about an AI feature you shipped and how you measured its quality. Our process has four steps: a 30-minute intro call, a technical conversation about systems you have built, a practical exercise on a realistic LLM feature, and a final conversation with the team you would join. We aim to give feedback within a few days of each step.
Junior vs mid vs senior AI engineer
A demo is easy to build. Seniority in AI engineering shows in how features behave with messy real data, adversarial users and model changes.
| Level | Scope | Typical experience | Key skills |
|---|
| Junior | Prompt and integration changes inside existing AI features, with review | 0-2 years | Python or TypeScript, calling model APIs, basic prompting, writing tests |
| Mid-level | Owns one AI feature end to end, including retrieval and evals | 2-5 years | RAG pipelines, structured outputs, tool calling, tracing, eval sets, cost tracking |
| Senior | AI architecture across features, quality standards, safety and provider strategy | 5+ years (1-2 with LLMs) | Agent design, eval methodology, prompt injection defenses, model routing, product judgment |
Tips for writing an AI engineer job description that attracts senior talent
- Describe a real feature, not "AI strategy". "An assistant that drafts responses from 40,000 support articles" is concrete. Use your actual use case and numbers.
- Say how you measure quality today. If you have no evals, say building them is part of the job. Senior AI engineers look for teams that take evaluation seriously.
- Name your model providers and data constraints. Whether data can leave your cloud, which providers are approved and whether you self-host models shape every design choice.
- Do not ask for ten years of LLM experience. The field is young. Ask for strong software engineering plus recent, real LLM shipping experience.
- Separate this role from ML research. If the person will not train or fine-tune models, say so. It keeps research candidates from applying and leaving.
- Mention the product surface. Say whether the feature is customer-facing, internal or an API, and whether the engineer will touch frontend code.
- Be clear about security review. If prompt injection, tenant isolation and audit requirements apply, list them. Experienced candidates see that as a sign of a serious team.
Skip the job post: hire a vetted senior AI engineer
Engineers who have shipped AI features to real users are in high demand, and hiring through job posts is slow. Ryz Labs can match you with senior AI engineers from Latin America who work on your team, work in your repos, product and standups, and keep hours within ±1h of US time zones. Only the top 1% of the engineers we interview make it through our vetting, which covers software engineering fundamentals, retrieval, evaluation and production LLM work.
Our staff augmentation model lets you add one AI engineer or several. Ryz engineers work on your team, reporting to your leads. Talk to us to scope your team. If you need a whole AI system built, Ryz AI pod teams, dedicated pods of senior engineers that include a tech lead, ML and backend engineers, build it inside your cloud and repos alongside your team. Hiring on your own? Our AI engineer interview questions cover what we test.
FAQ
What does an AI engineer do?
An AI engineer builds product features on top of foundation models: retrieval over company data, assistants, agents and structured extraction. They own prompts, retrieval, tool integration, evaluation, guardrails, cost and reliability, and they work like any other product engineer on shipping and maintaining features.
What is the difference between an AI engineer and an LLM engineer?
AI engineers focus on shipping product features with model APIs. LLM engineers go deeper into the models themselves: fine-tuning, inference optimization, serving open-weight models and large-scale evaluation harnesses. Many teams start with AI engineers and add LLM engineers when they need custom models or self-hosting.
Should an AI engineer job description require machine learning experience?
Not usually. Strong software engineering and hands-on LLM product experience matter more for most AI engineer roles. Basic ML literacy, such as understanding embeddings, evaluation metrics and overfitting, is useful and worth listing as preferred.
Questions we didn't answer? Email info@ryzlabs.com.