Building and shipping an AI solution is an exciting journey, but nobody handed you a roster to build an AI team.

Instead, most organizations respond by building their AI and ML engineering staff one role at a time, as issues pile up. They add a forward-deployed engineer here, maybe a data scientist there, and somehow that person is supposed to architect the system, handle security, and convince business stakeholders that this thing actually works. It rarely does.

Here’s the thing. A real AI center of excellence (COE), an operating model that delivers AI solutions aligned with the business? Central to this idea is having the right mix of AI team roles at the right time, including AI and GenAI Architects and Engineering Leadership, Applied AI and ML Scientists, ML Platform, MLOps and AI Infrastructure Engineers, AI Product and Adoption Leadership, and AI-Enabled and Product Software Engineers. 

The order matters too. Your first hires depend on AI maturity, how far AI is implemented in your operations. If this sounds like a lot, don't worry. Let's get into it.

Key Takeaways

• Build an AI team across five families: Architects, Scientists, Platform/MLOps, Product and Adoption Leadership, and AI-Enabled Engineers. This is what gets you from pilot to production and keeps you there.
• Use your AI maturity- how far your AI product is implemented in corporate operations, to determine your first hire.
• Deciding whether to build in-house, contract, or partner with a vendor is essential. A bad hire runs 30% to 150% of annual salary
• A job description is ten percent of the job and a resume is ten percent of the person. The candidate pool that can actually collaborate with other AI team roles and ship a reliable product is smaller than you think.

Why an AI Team Is Not One Role

When creating an AI team, the pith of your AI COE, you will get more steady production by hiring the right combination of capabilities and teamwork. McKinsey found that only 39% of organizations associate any level of enterprise-wide EBIT to AI's impact. And of that group, less than 5% see it show up in a meaningful way. That's not a technology problem. That's a people problem.

Think about it like baseball. You wouldn't field nine designated hitters and expect to win the pennant, right? Like the nine positions on a diamond, an AI team needs five distinct families of roles to compete. The pitcher, the shortstop, the catcher. They do different things. The same idea applies here.

When you're building an AI center of excellence (COE), you need people who handle security, people who build the blueprint, people who reach the doubters and naysayers in your organization. This is not just about that forward-deployed engineer who fixes your code or supports your infrastructure, but a Five-Family AI Team.

The Five Families of an Enterprise AI Team

The five families of an enterprise AI team optimize the ideal mix of skill sets and assistance to meet your AI project mandate. Let me walk you through each one.

AI and GenAI Architects and Engineering Leadership 

In team AI, AI and GenAI Architects and Engineering Leadership decide what to build and how to do it in a scalable, secure, and compliant way. They own the AI development blueprint and roadmap.

These senior technologists, e.g., AI Development Architect (Enterprise Cloud and AI):

• Design enterprise-scale GenAI solutions, LLMs, embeddings, and retrieval-augmented generation (RAG) pipelines which integrate into existing Azure / AWS / GCP ecosystems
• Own the end-to-end AI lifecycle, which includes data, model training and fine-tuning, evaluation, deployment, and observability (MLOps / LLMOps).
• Lead proofs-of-concept for AI-first use cases and implement the ones that work.
• Collaborate with clients to shape AI roadmaps, respond to RFPs, run architecture reviews, and make technical choices that translate to business value and ROI
• Build the practice, mentor AI team roles, and create reusable AI accelerators, standards, and governance

We screen for:

• 15–20+ years across software engineering and solution architecture
• Hands-on AI/ML and GenAI system design
• Fluency in LLMs, vector databases, and orchestration frameworks (LangChain, LangGraph, LlamaIndex)
• RAG, LoRA/QLoRA fine-tuning
• Prompt engineering
• Cloud-native AI platforms (Azure OpenAI, AWS Bedrock, Vertex AI)
• Communication skills to interact with a CIO

Applied AI and ML Scientists

Applied AI and ML Scientists are your data science team. They take the frontier AI models and proprietary data and make them work together.

These AI roles, e.g., applied AI research, use their skills to:

• Design and implement custom AI solutions by integrating frontier models with the client’s proprietary data to build retrieval tools and LLM agents
• Architect agentic, multi-agent workflows (LangGraph, CrewAI, AutoGen, Google ADK) that handle multi-step reasoning, tool use, and self-correction
• Do deep NLP and computer-vision work, including entity and relation extraction, coreference resolution, knowledge graphs, OCR/HTR, and document-layout analysis.
• Evaluate and optimize multi-modal foundational models (Gemini, Claude, GPT, Qwen) and stand up “LLM-as-a-Judge” and observability frameworks to catch hallucinations, drifts, and biases
• Improve model performance through fine-tuning, RLHF, and inference optimization (vLLM, LoRA/QLoRA, quantization), and often contribute to published research

Hire this type based on:

• An MS/PhD in a quantitative field (or equivalent shipped work)
• A command of ML fundamentals
• Practical model training, fine-tuning, and deployment
• Application of the ReAct framework and agentic patterns
• Use of embeddings and vector databases
• Strong Python skills
• A portfolio showing strong business-related GenAI accomplishments

ML Platform, MLOps and AI Infrastructure

ML Platform, MLOps and AI Infrastructure Engineers make AI reliable at scale. They are the ones keeping it going on a Tuesday at 2 a.m., and within budget. They, e.g., an AI senior platform engineer:

• Design scalable ML workflows across development, training, and production, including feature pipelines, training orchestration, and reproducible iteration.
• Own model deployment, versioning, rollback, and promotion
• Manage the lifecycle of datasets, model artifacts, and inference endpoints.
• Stand up LLM infrastructure, including prompt/version management, retrieval workflows, evaluation harnesses, and latency/cost monitoring
• Own cloud architecture and Infrastructure-as-Code (Terraform), security and compliance hardening, and multi-tenant data separation for high-value customers
• Drive observability and governance standards through monitoring, detecting drifts, evaluating, and auditing across ML and LLM systems

When you hire MLOps engineers, look for: 

• 6+ years in Applied ML / MLOps / platform engineering
• Production LLM experience (inference patterns, cost/latency trade-offs)
• Terraform and deep GCP or AWS/Azure
• Serverless and AI infrastructure (FaaS, LLMs, vector databases);
• Orchestration tools (Airflow, Prefect, Dagster, Kubeflow)
• Application of model registries (MLflow) and containerized deployment
• Strong Python skills

AI Product and Adoption Leadership

The AI Product and Adoption Leadership works with the corporate culture to make AI stick across business operations. They, e.g., an AI Center-of-Excellence Product Lead

• Partner with business-unit leaders to identify AI use cases, assess readiness, and build adoption plans that work
• Own a prioritized AI roadmap that translates business problems into AI opportunities and moves them from concept to production, with engineering and data teams
• Run change management and enablement training and playbooks for frontline associates to senior leaders
• Support responsible-AI governance and responsible AI implementation,  in partnership with legal and security teams.
• Define success metrics that go beyond model accuracy to address business needs and report this portfolio health to leaders

Screen for several AI capabilities.

• 8+ years in product management, technology consulting, or a techno-functional role with real AI/data exposure
• Working knowledge of GenAI, LLMs, and agentic workflows (enough to evaluate limits and guide safe deployment); 
• Executive communication skills
• The ability to influence without authority in a matrixed org
• Comfort operating in ambiguity
• Proficiency with Azure /enterprise AI tooling and experience with Responsible-AI

AI-Enabled and Product Software Engineers

As a family of AI engineers, the AI-Enabled and Product Software Engineers ship the product around the AI model. They, e.g., the forward-deployed engineer,:

• Build production software that powers inference, optimization, and AI services, from end to end ( prototype to deployment, as part of a SaaS offering).
• Engineer data pipelines and orchestration for messy, multimodal, geospatial, and vector inputs into AI systems.
• Ship the user-facing product through interactive web apps and workflows (Python / React), work forward-deployed with high-value customers to solve adoption reluctance
• Practice AI-assisted development by translating requirements into structured prompts.
• Review, harden, and validate AI-generated code to catch hallucinations, security risks, and gaps.
• Set AI-augmented engineering standards, including prompt guidelines, review workflows, and governance, while owning architecture, testing, and CI/CD.

Screen the role type based on:

• 3–5+ years of experience building large, complex systems; strong Python (plus React / JavaScript, or C#/.NET)
• Handling production backends, APIs, and distributed services
• Hands-on fluency with AI coding tools (Cursor, GitHub, Copilot, Claude, Kiro)
• Outstanding customer communication.

Who To Hire First, Sequencing an AI Team by Maturity

Your first hire depends on AI maturity—how far AI is implemented in your AI team’s operations. Following an MLOps engineering approach, sequence your hires to match your stage.

Here's the classic mistake. Companies in the exploring phase hire a data science team without the architect or ML engineers to ship anything. They burn the budget building an AI team and end up with a model in search of a problem. It's like signing five power hitters without a pitching rotation.

A better path is onboarding the architects first to own the blueprint, and then contracting ML engineers to build against it. Over the last 60 days, MSH has used this sequence to ensure shippability.

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Build In-House, Contract, or Partner

New AI leaders tend to turn to existing AI talent or train their ML engineers when building an AI center of excellence. They see it as cost-effective and easier to proceed. Few think about contracting through or partnering with a vendor. We’ve seen these options work for firms. Let’s walk through the pros and cons of each.

Full-Time Build

Building AI teams from scratch works for businesses that produce an AI self-service software product with no services attached, or for very small businesses who don’t need additional technical staff in the next year or two. However, corporations that need to ship within tight deadlines find progress stalls. In the best-case scenario, it takes six months to a year to find your person in an AI talent bottleneck.

Contract and Contract-to-Hire

When you need to validate a use case or hire for a specialized skill set, contract the AI talent. This approach provides a dedicated resource quickly, for the short term and a possible long-term hire once the ROI is proven. However, when the contract ends and the ML engineer leaves, their talent and knowledge walk out the door with them.

Partner

Partnering with a nearshore team ensures that your business mandate will align with the AI solution. Companies have an onshore architect who designs the AI system according to the business strategy, and works with a nearshore or offshore ML team to bring it to life, within budget. The catch is you need a global partner who can mitigate currency swings, changing regulations, and local politics. Not everyone can do that well.

What To Screen For, and Why Resumes Miss It

However your organization decides to onboard your AI team, you want to get and keep the best talent. It’s one thing for an AI engineering candidate to explain how to leverage vector databases and quite another to have deployed an AI model that uses vector databases in production.

We’ve found it hard to distinguish the two types in a typical job search. CEO Oz Rashid finds that “a job description is ten percent of the job and a resume is ten percent of the person.” Uncovering that unique candidate with the AI skills you need is what a global partner does best.

FAQ

What roles do you need to build an AI team?

You need five distinct types of AI roles

• ‍AI and GenAI Architects and Engineering Leadership who decide what to build and how
• Applied AI and ML Scientists who make the models work
• ‍ML Platform, MLOps and AI Infrastructure Engineers who make AI reliable at scale
• ‍AI Product and Adoption Leadership who get AI to stick 
• ‍AI-Enabled and Product Software Engineers who ship the AI product

Who should be the first hire on an AI team?

Your first hire depends on the level of maturity. Consider architects first when exploring, scientists when building, product leadership when scaling, and platform engineers when improving efficiency.

How do you structure an enterprise AI team?

Structure it around an AI Center of Excellence (COE) with the five families mapped to four pillars: Architects own strategy, Platform/MLOps owns governance, Scientists and ML engineers deliver, and Product Leadership enables adoption.

Should you build an AI team in-house or use a partner?

If you are building an AI self-service software product with no services attached, or you're a very small company that doesn’t anticipate additional staff in the next year or two, build in-house. However, this approach is slow and expensive when you need to implement AI on a tight timeline.  Partnering with a global nearshore team offers a cost-effective and efficient way to build agentic AI to your business strategy.

How big should an AI team be?

An AI team should encompass at least five types of AI talent.

What is an AI Center of Excellence?

AI Center of Excellence (COE) is an operating model that provides a shared set of practices, tools, and expertise.

Big Takeaway

Building an AI team requires staffing at least five roles for your AI center of excellence to operate efficiently and avoid stalling.

Learn more about ML and AI engineer recruiting services today. Learn more about ML and AI engineer recruiting services today.