Templates

AI team structure template: roles, pod shape and RACI

How to structure the people who build AI: core roles, a pod shape that ships to production, who owns what in a RACI, and when to centralize or spread AI work.

By the Ryz Labs team · Updated October 2026

An AI engineering team is usually built as a pod: a tech lead, AI or LLM engineers, a backend engineer, a data engineer and, when you train or host models, an ML or MLOps engineer, working with a product owner from the business. Pods own a use case from data access to production monitoring. As the number of use cases grows, companies add a small central platform group for shared evaluation, model access and guardrails, while pods stay close to the business teams they serve. The template below gives you the roles, a pod shape, a RACI and the operating model choices.

AI team structure template

1. AI team roles

Role	Owns	Needed when	Can be combined with
AI tech lead	Architecture, provider and model choices, eval standards, technical risk	From the first use case	Senior AI engineer on a first pod
AI or LLM engineer	Retrieval, prompts, tool calling, agents, evals, guardrails for features	From the first use case	Backend engineer for small features
Backend engineer	APIs, integrations, auth, the product surfaces AI features live in	From the first use case	AI engineer
Data engineer	Pipelines, document parsing, permissions, data freshness	When features use internal data (most do)	None for long; data work grows fast
ML engineer	Fine-tuning, custom models, inference optimization	When you train, fine-tune or self-host	MLOps engineer
MLOps or platform engineer	Deployment, monitoring, versioning of models and prompts, cost tracking	With several AI features in production	Platform team
Product owner (business side)	The problem, success metrics, user feedback, adoption	Always	Product manager
Evaluation owner	Eval sets, quality reviews, regression checks	When quality decides adoption (always)	AI tech lead or product owner
Security and risk partner	Data access approvals, prompt injection review, compliance sign-off	Before production in regulated work	Existing security team

2. Example pod shape for a production use case

Seat	Count	Seniority	First 30 days focus
Tech lead	1	Senior or staff	Architecture, eval plan, access to data and environments
AI or LLM engineers	2	Senior	Retrieval or agent prototype on real data, first eval set
Backend engineer	1	Senior	Integration with the product and auth
Data engineer	1	Senior	Pipelines from source systems with permissions
ML or MLOps engineer	1	Senior	Tracing, cost tracking, deployment path
QA or evaluation engineer	1	Mid-level or senior	Test harness and regression checks

3. RACI for an AI feature (R = responsible, A = accountable, C = consulted, I = informed)

Activity	Product owner	AI tech lead	AI engineers	Data engineer	MLOps	Security
Define the problem and success metric	A	C	C	I	I	I
Approve data access	C	R	I	R	I	A
Build the eval set	C	A	R	C	I	I
Choose model and provider	I	A	R	I	C	C
Build retrieval and features	I	A	R	R	C	I
Production readiness review	C	A	R	R	R	C
Monitor quality and cost after launch	C	A	R	C	R	I

Use this template

How to use this AI team structure template

List your AI use cases for the next two quarters and the approach for each: model APIs with retrieval, agents, fine-tuning or custom models.
Use the roles table to mark which seats each use case needs. Most API-based use cases need the first four roles plus a product owner and an evaluation owner.
Decide how many pods you need. One pod can usually carry one complex use case or a few small related ones.
Fill the RACI with names. Every row needs exactly one "A"; if two people are accountable, nobody is.
Choose an operating model: one central team while you have one or two use cases, then pods aligned to business areas with a small central platform group as use cases multiply.
Revisit the structure after each production launch, when you know which roles were bottlenecks.

Example: first two use cases

A company plans two use cases: an internal knowledge assistant for operations staff and automated extraction of fields from inbound documents. Both use model APIs with company data.

Decision	Choice
Number of pods	One pod covering both use cases, since they share document parsing and retrieval
Seats	Tech lead, 2 AI engineers, backend engineer, data engineer; MLOps work shared with the existing platform team
Accountable for quality	AI tech lead, with the operations lead as product owner
Not yet needed	ML engineer (no fine-tuning planned)

Common AI team structure mistakes

A data science team asked to ship products. Data scientists answer questions with data. Shipping AI features to users is software engineering, and needs engineers who own production.
No business owner. AI features without a product owner from the business ship and then sit unused.
Evaluation owned by nobody. If nobody owns the eval set, quality is judged by demos and anecdotes.
Centralizing for too long. A single central AI team becomes a queue once several business areas want use cases.
Decentralizing too early. With one or two use cases, separate AI people in every team duplicate tooling and never share patterns.

Need a pod that ships?

Ryz AI pod teams are dedicated pods of senior engineers, typically a tech lead with AI, ML and backend engineers, that build AI systems in your cloud and repos and ship them to production alongside your team. Pods join your repos, CI, standups and weekly demos, and can grow or hand the system over to your engineers when you are ready. Only the top 1% of the tens of thousands of engineers we interview make it. Typical cost is $7,000 to $15,000 per engineer per month. See AI pod teams, forward-deployed engineers and our case studies, or talk to us to scope your pod.

FAQ

What roles are needed in an AI team?

Most AI engineering teams need an AI tech lead, AI or LLM engineers, a backend engineer and a data engineer, plus a product owner from the business and someone who owns evaluation. Add ML engineers for fine-tuning or custom models and MLOps engineers once several AI features run in production.

What is an AI pod team?

An AI pod is a small, dedicated team of engineers that owns an AI use case end to end, from data access and model choice to production monitoring. A typical pod has a tech lead with AI, ML, backend and data engineers, and works with a business product owner.

Should AI teams be centralized or spread across the business?

Start centralized while you have one or two use cases, so patterns and tooling are shared. As use cases multiply, align pods to business areas and keep a small central group for shared evaluation, model access, guardrails and cost tracking.

Who should own AI quality?

One accountable person, usually the AI tech lead, with an eval set built from real user tasks. The business product owner defines what good means, and engineers keep the eval set current as the feature changes.

How big should an AI team be?

A production use case on model APIs is often carried by a pod of four to seven engineers. A first prototype can start smaller, but plan for data, backend and operations work, which usually exceeds the model work.

Questions we didn't answer? Email info@ryzlabs.com.