Getting an AI model ready for production is quite an accomplishment.

Keeping it there with stable data quality and reproducible behavior is another.

You need MLOps, which is the discipline of shipping and maintaining the AI product end to end. But here's what happens. ML principles are only as good as the people running it.

You need a core team with real judgment and real accountability, and people who own outcomes in production. That's the whole game.

Every MLOps member keeps the AI usable by reducing technical debt. They ensure data quality, reproducibility, easy maintenance, accessibility, and evolution as marketplace conditions change in real time. The secret is to get a good mix of specializations on your team.

Key Takeaways

• A reliable core MLOps team consists of seven roles. They are the MLOps Engineer, ML Engineer, Data Scientist, Data Engineer, ML or Platform Architect, DevOps or Platform Engineer, and Product and Domain owner.
• The first hire depends on AI maturity. Where many businesses are at level 0 or 1, they would benefit from contracting an ML engineer or MLOps engineer to stand up automated MLOps pipeline processes.
• Working together, the core MLOps team reduces technical debt by addressing multiple components that interact with the AI model.
• Hiring MLOps team members who have built, integrated, and tested an MLOps pipeline is challenging because it requires more data points than an MLOps job description gathers.

What Is MLOps and Why Does It Need More Than One Role?

MLOps is a methodology that takes a generative AI model from a business goal to a reliable, cost-controlled, monitored solution in production. Based on MLOps principles, it deals with the technical debt that agentic AI carries into production.

ML agentic AI systems in production carry technical debt. They:

• Interact with other components, including other AI modules
• Process data more intensively than traditional software
• Amplify reasoning errors as they compound
• Generate code for other modules
• Drift into misconfigurations over time

Given a complex ML infrastructure in production, Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027. 

We’ve found that having the right mix of roles and responsibilities, through a reliable core MLOps team, mitigates this technical debt. It is one of the reasons why your ML application works for  the business professional who transacts with the AI model, months later at 2 a.m. on a Tuesday.

The Core MLOps Roles and What Each One Owns

A reliable core MLOps team has seven distinct MLOps roles and responsibilities.

MLOps Engineer 

The MLOps engineer ensures the ML models perform reliably and can scale in production. They do this through pipelines, deployment, observability, security, and cost.

An MLOps engineer:

• Owns model deployment, versioning, rollback, and promotion; manages the lifecycle of datasets, model artifacts, and inference endpoints
• Stands up LLM infrastructure — prompt/version management, retrieval workflows, evaluation harnesses, and latency/cost monitoring
• Owns the cloud architecture and Infrastructure-as-Code (Terraform), security and compliance hardening, and multi-tenant data separation for high-value customers

Screen MLOps skills 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 experience
• Serverless and AI infrastructure (FaaS, LLMs, vector databases) capabilities
• Application of orchestration tools (Airflow, Prefect, Dagster, Kubeflow)
• Model registries (MLflow) handling
• Containerized deployment
• Strong Python coding skills

ML Engineer 

An ML engineer differs from an MLOps engineer in focus. The ML engineer implements and ships the agentic AI model to business specifications.

An ML engineer:

• Builds the production software that powers inference, optimization, and AI services, covering everything from prototype to deployment, as part of a SaaS offering.
• Engineers the data pipelines and orchestration for messy, multimodal, geospatial, and vector data feeding AI systems
• Ships user-facing products through interactive web apps and workflows (Python / React).
• Works forward-deployed with high-value customers to solve the “last-mile” adoption problem

We screen an ML engineer for:

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

Data Scientist

A data scientist takes the frontier models and proprietary data and turns them into systems

that reason, extract, and decide.

This role:

• Architects agentic, multi-agent workflows (LangGraph, CrewAI, AutoGen, Google ADK) that handle multi-step reasoning, tool use, and self-correction
• Does the deep NLP and computer-vision work, including entity and relation extraction, coreference resolution, knowledge graphs, OCR/HTR, and document-layout analysis
• Evaluates and optimizes multi-modal models (Gemini, Claude, GPT, Qwen) and stands up “LLM-as-a-Judge” and observability frameworks to catch hallucinations, drift, and bias

When hiring a data scientist, we look for: 

• An MS/PhD in a quantitative field (or equivalent shipped work)
• Command of ML fundamentals
• Practical model training
• Fine-tuning and deployment
• Application of the ReAct framework and agentic patterns
• Embeddings and vector databases
• Strong Python coding skills
• A portfolio of real GenAI accomplishments — not just coursework

Data Engineer

A data engineer maintains and builds your data architecture infrastructure, so that it works when you need analysis.

This kind of specialist:

• Reduces data pipeline processing time
• Migrates ETL jobs and improves efficiency
• Designs and implements real-time data processing, reducing latency

We screen for the following:

• Strong Python (PySpark) and Java (required) skills
• Advanced SQL capabilities
• AWS Cloud services (Redshift, EMR, Glue) capabilities
• Real-time pipeline handling using Kafka, Kinesis
• Orchestration (Airflow or similar tools) experience
• Version control using CI/CD practices in Git
• Terraform for infrastructure as code
• Application of observability tools: Prometheus and Grafana

ML or Platform Architect

The ML or Platform Architect translates the business problem into a solution by architecting AI solutions that are scalable, secure, compliant, and actually deliverable

In the AI/ML team, these senior technologists,

• Lead proofs-of-concept for AI-first use cases, which include intelligent automation, code generation, knowledge assistants, and predictive analytics
• Interact with clients to shape AI roadmaps, respond to RFPs, run architecture reviews, and translate deep technical choices into business value and ROI
• Build the MLOps practice by mentoring architects, engineers, and data scientists, and creating reusable AI accelerators, standards, and governance

This AI team role is vetted 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 capabilities
• Prompt engineering
• Applications of Cloud-native AI platforms (Azure OpenAI, AWS Bedrock, Vertex AI)
• Communication skills to carry a CIO conversation

DevOps or Platform Engineer

The DevOps or Platform Engineer is the intermediary between development and operations, whether automated or ML. They support developers in their CI/CD workflow. 

Day-to-day, a DevOps team member:

• Develops and manages CI/CD pipelines
• Automates, through Terraform or Kubernetes, to deploy scalable infrastructure 
• Collaborates across development and operations teams to integrate and ease workflows

Screen the DevOps or Platform Engineer for:

• A strong affinity for cloud platforms such as AWS, Azure, or GCP

• Hands-on experience with IaC (Terraform, Ansible) and CI/CD (Jenkins, GitHub Actions).
• Deep knowledge of scripting (Python, Bash, YAML)
• Capabilities to automate with Copilot and ChatGPT
• Familiarity with orchestration of containers using Kubernetes

Product and Domain Owner

The product and domain owner leads adoption of the AI solution and ensures business ROI and responsible use.

This role:

• Partners with business-unit leaders to identify AI use cases, assess readiness, and build adoption plans that work
• Owns a prioritized AI roadmap that translates business problems into AI opportunities and moves initiatives from conception to production with engineering and data teams.
• Runs change management and enablement through training, playbooks, and internal evangelism for frontline associates to senior leaders.

As one of the AI team roles, the Product and Domain owner needs:

• 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 an ambiguous environment
• Azure/enterprise AI tooling and Responsible-AI Experience

Which MLOps Role To Hire First

When AI operations act inconsistently in production, and the business is unhappy, organizations stack up data engineers or other specialists to fix the ML code underneath the AI product. Unfortunately, this approach overlooks technical debt and the MLOps pipelines needed for running the agentic AI app.

A better plan is to start with an adaptable ML or MLOps engineer, matched with the MLOps maturity. These kinds of MLOps roles and responsibilities fix the app and build and implement MLOps pipelines, keeping the ML model continuously running.

We base this suggestion on our observations that many businesses with production issues are at MLOps level zero (0), relying on manual processes, or level one (1), having an ML pipeline that deploys the source control code, but needs to automate continuous training. 

Timeliness in moving an agentic AI app from stalling to running is critical. One cost-effective way  is to contract out a multipurpose MLOps engineer with a nearshore team partner. This way provides an MLOps contractor engineer, available within 72 hours of request. Once that person gets your AI operations up and running in production, you can hire that professional as part of your MLOps team.

How MLOps Roles Change as the Team Scales

Machine Learning operations evolve by stage, impacting the objectives to run the AI model reliably in production.  We start with an architected ML model and CI/CD and build in real-time observability, governance, retraining, and AI model advancement capabilities through the MLOps pipelines. To keep pace, the MLOps team moves from generalist ML engineers and MLOps engineers to more specialized MLOps roles and responsibilities.

MLOps Role by Stage

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The Hard Part Is Not the Org Chart, It Is Finding the People

Getting the AI model running in production, and keeping it there, requires the best mix of MLOps roles and responsibilities. Gartner predicts at least 15% of day-to-day work decisions will be made autonomously through agentic AI by 2028, up from 0% in 2024.

The prize is real. But traditional job boards underperform, and resumes hide whether the candidate has actually shipped a production system.

Oz Rashid, MSH's CEO, puts it simply: collect data points and suspend judgment. The right MLOps engineers reveal themselves through performance—self-directed, analytically sharp, accountable for outcomes.

Identifying them requires access to people who have built, integrated, and tested an MLOps pipeline, running agentic AI in production. A structured and consultative approach uncovers them.

Frequently Asked Questions

What roles do you need on an MLOps team?

The roles you need depend on your MLOps maturity stage, including startup, scaling, and enterprise. In all stages, you need ML engineers and MLOps engineers to standup MLOps pipelines with CI/CD and continuous training capabilities.

Who is responsible for deploying and monitoring machine learning models in production?

The MLOps engineer is responsible for deploying and monitoring machine learning models in production.

What is the difference between an ML engineer, an MLOps engineer, and a data scientist?

An ML engineer implements and ships the AI model. The MLOps engineer ensures the ML models perform reliably. The data scientist takes the frontier models and corporate data and turns them into systems that reason, extract, and decide. 

What skills does an MLOps engineer need?

An MLOps engineer needs 

• 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) capabilities
• Experience with orchestration tools (Airflow, Prefect, Dagster, Kubeflow)
• Capabilities with model registries (MLflow)
• Containerized deployment
• Strong Python coding skills

How many people do you need on an MLOps team?

Fully staffed, you need seven types of ML roles and responsibilities.

Do you need MLOps if you only have a few models?

Yes. You need MLOps, even if you only have a few AI models. Otherwise, your ML models become susceptible to technical debt and inconsistent AI executions.

Big Takeaway

For ML models to run in production, you need to have the right MLOps roles and responsibilities. They reduce technical debt and automate MLOps pipelines to support the infrastructure around the AI models and their data interactions. Finding the best AI engineering talent to get to these goals is only one step away.