Artificial intelligence is rapidly moving from experimentation into the operating core of the enterprise.
Organizations are no longer managing a handful of isolated machine-learning models.
They are operating generative AI applications, enterprise copilots, retrieval systems, foundation-model APIs, AI agents, intelligent workflows, internal AI platforms, specialized models, vector databases, evaluation frameworks, and third-party AI services—often across multiple business functions simultaneously.
That transition creates an entirely new operational challenge.
An AI system can technically be online and still be failing.
The infrastructure may be healthy while response quality deteriorates.
A model provider may change behavior without a traditional application deployment.
An agent may successfully complete thousands of tasks before an edge case exposes an unexpected control weakness.
Token consumption can increase rapidly.
Retrieval quality can deteriorate as enterprise knowledge changes.
Latency can increase while traditional infrastructure metrics remain within acceptable thresholds.
And a seemingly small prompt, model, policy, data, or configuration change can materially alter how an AI application behaves.
We are seeking an experienced AI Operations Manager to build the operational discipline required to run enterprise AI reliably, securely, responsibly, and economically at scale.
This position will sit at the intersection of Artificial Intelligence, Machine Learning Engineering, Platform Engineering, Cloud Infrastructure, Data, Cybersecurity, Risk, FinOps, Product, and Business Operations.
The AI Operations Manager will help establish how production AI systems are deployed, monitored, evaluated, governed, supported, optimized, and continuously improved.
The mandate is substantially broader than traditional infrastructure operations.
You will need to understand both system health and AI behavior.
That means monitoring conventional signals such as uptime, latency, throughput, errors, capacity, and infrastructure utilization while also establishing visibility into AI-specific signals such as response quality, hallucination rates, retrieval performance, model drift, token utilization, inference cost, safety violations, evaluation results, agent completion rates, escalation patterns, and user adoption.
Generative AI will be a major component of the role.
The environment may include enterprise use of platforms and models from leading AI providers alongside open-source models, internally developed solutions, retrieval-augmented generation, AI gateways, vector databases, orchestration frameworks, and emerging agentic systems.
The AI Operations Manager will help ensure these capabilities can move safely from prototype → controlled deployment → production → enterprise scale.
AI governance will be equally important.
This leader will partner with Security, Legal, Privacy, Risk, Compliance, Data Governance, and business stakeholders to establish practical controls around model access, sensitive information, third-party AI services, responsible AI, human oversight, logging, evaluation, change management, and incident response.
The goal is not to create governance that prevents innovation.
The goal is to create an operating environment where the organization can innovate faster because appropriate controls already exist.
Economics will also matter.
As enterprise AI adoption grows, inference, model API, GPU, storage, retrieval, observability, and vendor costs can become significant. This role will build visibility into those economics and help leadership understand the cost of AI by application, model, workflow, business unit, or user population.
The successful candidate will combine the mindset of an SRE leader, AI platform operator, technology strategist, incident commander, governance partner, and business operator.
You should be comfortable discussing an LLM evaluation framework with AI engineers, investigating a production incident with platform teams, reviewing token economics with Finance, challenging an AI vendor’s SLA, discussing data controls with Security, and presenting AI operational health to senior leadership.
Ultimately, this role answers one critical enterprise question:
How do we make AI dependable enough to become part of how the company actually operates?
Establish operating standards for production AI applications and services.
Treat AI reliability as an engineering discipline rather than reactive support.
Develop operational practices supporting enterprise generative AI.
The environment may involve:
Establish consistent processes for configuration, deployment, evaluation, monitoring, rollback, and lifecycle management.
Develop operating controls for increasingly autonomous AI systems.
Support environments involving:
Establish controls around:
Identity → Permissions → Tool Access → Data Access → Action Boundaries → Human Approval → Logging → Evaluation
Monitor agent completion rates, failures, unexpected behaviors, escalations, and downstream business impact.
Build comprehensive visibility into AI system behavior.
Develop dashboards and monitoring for:
Integrate AI observability with broader enterprise monitoring wherever practical.
Partner with AI Engineering, Data Science, Product, and business teams to establish repeatable evaluation processes.
Support:
Prevent model, prompt, retrieval, or configuration changes from reaching production without appropriate validation.
Establish a mature incident-management model for AI services.
Coordinate response to events involving:
Lead structured incident response.
Ensure significant incidents produce:
Containment → Root Cause → Corrective Action → Lessons Learned → Preventive Controls
Translate enterprise AI policies into practical operational controls.
Partner with:
Support controls involving:
Maintain sufficient evidence for internal reviews and audits.
Establish operational discipline across multiple model providers.
Evaluate:
Develop contingency strategies where critical applications depend heavily on a single provider or model.
Create financial visibility into enterprise AI consumption.
Track costs associated with:
Develop metrics such as:
Cost per User | Cost per Query | Cost per Workflow | Cost per Agent Task | Cost per Application
Identify optimization opportunities without materially degrading quality.
Partner with Finance and Technology leadership on AI budgeting and forecasting.
Support shared infrastructure used by multiple AI teams.
Potential capabilities may include:
Improve platform reliability while enabling engineering teams to deploy AI capabilities faster.
Partner with Security teams to implement appropriate AI controls.
Support:
Ensure employees and AI agents receive appropriate—not unlimited—access to enterprise resources.
Establish change processes appropriate for AI systems.
Manage operational risk associated with changes to:
Create appropriate testing, approval, deployment, and rollback procedures.
Partner with business and transformation teams to improve responsible adoption.
Identify:
Help distinguish meaningful productivity improvements from technology experimentation without measurable value.
Manage operational relationships with AI technology providers.
Provide leadership with evidence-based recommendations about AI technology investments.
Build a disciplined AI Operations function.
Depending on organizational structure, lead or coordinate professionals across:
Establish clear ownership across Engineering, Product, Data Science, Security, and Operations.
Eliminate ambiguity around who owns production AI after deployment.
Experience with several of the following is particularly valuable:
Experience operating enterprise AI environments involving leading commercial or open-source model ecosystems is strongly valued.
You know that a technically impressive AI system is not necessarily a production-ready system.
You understand dependencies across models, prompts, retrieval, data, infrastructure, APIs, security controls, and business workflows.
You remain structured and decisive when production behavior becomes uncertain.
You continuously study new models, platforms, agent frameworks, operational patterns, and AI capabilities without treating every new technology as automatically production-worthy.
You can distinguish acceptable experimentation from risk requiring immediate intervention.
You understand that AI architecture decisions can have significant unit-economic consequences.
You can explain complex AI operational issues without requiring executives to understand every technical implementation detail.
You can align engineers, data scientists, security professionals, business leaders, Finance, Legal, and vendors around shared operational standards.
You prefer eliminating recurring operational problems over repeatedly responding to them.
The AI Operations Manager will provide critical operational leadership across the enterprise AI portfolio.
Provide operational input before major AI investments move into production.
Translate policies into practical engineering and operational controls.
Help business functions move from isolated pilots to sustainable production workflows.
Provide operational perspectives on model, platform, provider, and infrastructure decisions.
Strengthen access, data, identity, logging, and third-party AI controls.
Provide visibility into AI consumption, capacity, and unit economics.
Help determine where to build, buy, partner, or maintain multiple model-provider options.
Measure whether AI adoption is producing meaningful operational improvement.
Ensure critical AI-enabled workflows have appropriate resilience and recovery strategies.
The role will help leadership connect:
AI Ambition → Production Reality → Operational Control → Business Value
Primary Function: Artificial Intelligence Operations & Platform Management
Operating Scope: Production AI + LLMOps + Reliability + Governance + FinOps
Core Expertise:
AI Operations | Generative AI | LLMOps | MLOps | AI Platforms | AI Agents | RAG | Model Evaluation | AI Observability | SRE | Incident Management | Cloud Infrastructure | AI Governance | Responsible AI | AI FinOps | Vendor Management | Enterprise AI Transformation
$220,000 – $255,000 annually
Final compensation will consider AI operations expertise, technical depth, production-scale experience, leadership scope, cloud and platform experience, generative AI knowledge, enterprise complexity, geographic considerations, and overall qualifications.
The broader total rewards package may include:
The next stage of enterprise AI will not be defined only by who has access to the most capable model.
It will be defined by who can make AI reliable enough, secure enough, measurable enough, economical enough, and trusted enough to operate at scale.
That is the problem this role is built to solve.
You will work beyond the experimental layer of AI.
You will help determine what happens after the prototype works.
How is it monitored?
How do we know the answers remain good?
What happens when the model changes?
What happens when an agent receives access to enterprise systems?
How quickly can we detect abnormal behavior?
What does each AI workflow actually cost?
How do we recover when something fails?
And how do we scale from a successful pilot to thousands of users without losing control?
Those questions will become increasingly important as AI moves deeper into everyday business operations.
You will have the opportunity to shape the operating model before many of those patterns become permanently established.
The mission is to move enterprise AI from:
Experiment → Deployment → Reliability → Governance → Scale → Measurable Value
And ultimately build the connection between:
Models + Data + Infrastructure + Controls + People → Trusted AI Operations
For a technology leader who wants to work where AI innovation meets production reality, this role offers the opportunity to build the operational foundation that allows an enterprise to use artificial intelligence with confidence.
At Admiral Talent, our vision is to redefine staffing through genuine partnerships, innovation, and a people-first mindset. Guided by experienced leadership, we’re committed to driving growth, empowering talent, and delivering exceptional results for every client we serve.