AI Operations Manager: complete hiring guide with job description

You’re hiring for the wrong skills. Everyone’s chasing ML engineers and data scientists, but Gartner’s research drops a number that should make you reconsider: only 1 in 10 organizations get 75% or more of their AI model prototypes into production.

That’s not a technology problem. It’s an operations problem.

The role nobody’s talking about (but everyone needs)

Here’s what most companies miss: AI Operations Managers don’t need to understand transformer architectures or gradient descent mathematics. They need to understand how your invoicing system talks to your inventory database, why your sales team refuses to use the CRM properly, and how to get IT and data science to actually collaborate instead of throwing requirements documents over the wall at each other.

Think of it this way - you wouldn’t hire a race car driver to manage your logistics fleet. Sure, they understand vehicles, but operational excellence requires different muscles entirely.

The primary function isn’t building AI. It’s making AI work within the messy reality of your existing business. Single Grain’s analysis nails it: these managers identify inefficiencies across teams and streamline processes using AI. Not build AI to streamline processes - that’s backwards.

What they actually do all day

Forget the job descriptions full of buzzwords. Here’s the real work:

System integration and monitoring - They’re watching dashboards like air traffic controllers, spotting when model performance drifts or when that “minor” API update breaks three downstream processes. They manage deployments, integration, and daily operations while everyone else is building the next shiny thing.

Translation services - Half their day is explaining to the CFO why the AI needs more compute budget, and the other half is explaining to data scientists why they can’t just “quickly update the model in production.” They collaborate with IT specialists, AI developers, data scientists, and senior management - often in the same meeting, speaking four different languages.

Process archaeology - Before any AI implementation, they dig through your actual workflows. Not the ones in your documentation (those are fiction), but the real ones. The Excel sheets your accounting team secretly maintains. The WhatsApp group where urgent decisions actually happen. The manual overrides that “never happen” but somehow happen daily.

Training and compliance - They teach your staff how to work with AI tools without breaking them, and ensure your AI systems don’t break laws or ethical guidelines. According to Workable, this includes developing training programs that actually stick, not just PowerPoint decks that gather dust.

The skills that matter (and the ones that don’t)

Industry research shows companies requiring 3-6 years of ML experience, but here’s the truth: you need someone with 5+ years of making broken systems work, regardless of whether those systems involved AI.

Essential skills:

• Systems thinking - seeing how changes ripple through your organization
• Crisis management - because models fail at 3 AM on Sundays
• Political navigation - getting budget and buy-in from skeptics
• Process optimization - finding the 20% of effort that delivers 80% of value
• Communication - explaining complex failures without using the word “algorithm”

Nice-to-have but not critical:

• Python programming (they’re not coding, they’re coordinating)
• Deep learning expertise (they’re managing people who have this)
• PhD in Computer Science (operational wisdom doesn’t come from academia)

MIT Sloan’s research found that companies need to focus 70% of their AI transformation effort on people-related capabilities, 20% on technology, and 10% on algorithms. Your AI Operations Manager is that 70%.

Why most AI projects fail (and how this role prevents it)

Fortune reported that 95% of generative AI pilots fail to scale to production. Not because the technology doesn’t work, but because organizations lack the operational infrastructure to support them.

The pattern is predictable. Data scientists build something impressive in a notebook. Everyone gets excited. Six months later, it’s still in the notebook because nobody figured out how to handle model versioning, data pipeline failures, or the fact that production data looks nothing like training data.

NTT DATA’s research puts the failure rate between 70-85% for GenAI deployment efforts. The primary culprits? Security gaps, governance issues, and organizational readiness - all operational challenges, not technical ones.

This is where your AI Operations Manager earns their salary. They build the boring stuff that makes AI work:

• Monitoring systems that catch drift before it becomes a crisis
• Rollback procedures for when (not if) something breaks
• Data quality checks that prevent garbage in, garbage out
• Change management processes that don’t assume everyone loves new technology

Gartner’s survey found that 45% of high-maturity AI organizations keep projects operational for three years or more, compared to just 20% in low-maturity organizations. That difference? Operational excellence.

How to hire the right person

Stop looking for unicorns with ML expertise AND operations experience AND business acumen. Instead, find someone who’s successfully managed complex technical operations and teach them the AI-specific bits.

Red flags in candidates:

• They lead with their technical credentials
• They talk about AI transformation without mentioning current processes
• They can’t explain a technical concept in business terms
• They’ve never had to maintain someone else’s system

Green flags to watch for:

• War stories about fixing inherited messes
• Questions about your current tech stack and processes
• Examples of getting hostile departments to collaborate
• Understanding of why documentation always lies

Interview questions that matter:

“Our AI model works perfectly in testing but fails randomly in production. Walk me through your investigation.” (You’re looking for systematic thinking, not jumping to conclusions)

“The data science team wants to update models daily. Operations wants monthly releases. How do you handle this?” (They should recognize this as a process problem, not a technical one)

“We have 15 different AI initiatives from different departments. How do you prioritize?” (Look for frameworks that consider business impact, not just technical elegance)

Companies getting it right:

Capital One is aggressively hiring for these roles, with over 100 positions focused on AI operations and governance. They’re specifically looking for people who understand vendor evaluation, multi-agent systems, and AI observability - notice how none of those require building models from scratch.

Walmart’s CEO openly states that AI is changing every job, and they need managers who understand both human and technical skills. Their focus? People who can implement AI tools that track everything from sales trends to supply chain logistics, not people who can build those tools.

The Harvard Business School research shows AI is flattening hierarchies and changing what management means. Your AI Operations Manager needs to thrive in this ambiguity, managing both human teams and AI systems that increasingly handle coordination tasks traditionally done by middle management.

Look for operational excellence first, technical competence second. Someone who’s successfully managed a complex warehouse operation might be a better fit than someone with a PhD in machine learning who’s never dealt with production systems.

Remember: you’re not hiring them to build AI. You’re hiring them to make AI work in your organization. Those are vastly different jobs, and confusing them is why 42% of companies abandoned the majority of their AI initiatives in 2025.

The best AI Operations Manager you can hire is probably managing something else right now - supply chains, IT infrastructure, or manufacturing operations. They understand systems, dependencies, and the messy reality of keeping complex operations running.

Teach them AI. Don’t try to teach an AI expert operations. One of those paths is much shorter than the other.