AI talent
MLOps and LLMOps Engineer
Makes AI systems deployable, observable and reversible: pipelines, evaluation in CI, monitoring and cost control.
The role
What the job actually is.
This role turns a working model into something an operations team can own. Deployment pipelines, evaluation that runs automatically rather than when someone remembers, monitoring that watches output quality and spend as well as uptime, and a route back to the previous version that has actually been used. On generative systems the cost limb matters more than teams expect, because spend scales with usage rather than with headcount and nobody notices until the invoice. Reversibility is the test: a system that cannot be rolled back is not in production, it is merely live.
What we screen for
The questions that separate the field.
Asked by someone who has built the thing, and designed to catch this role's specific failure rather than to confirm a general impression.
- Whether they have run a rollback in anger, and what made it necessary.
- What they monitor beyond uptime. Output quality and cost per interaction, or neither.
- Whether evaluation runs automatically in CI or by hand before a release.
- How they detect drift, and what threshold triggers a human.
The common mis-hire
The one you have probably already made.
A DevOps engineer who has containerised a model and never watched one degrade. The pipeline is competent, the observability stops at the infrastructure layer, and the first quality regression is reported by a user.
In the estate
Where this role works, and what we screen it against.
The layers this family works at, lit. These are the tools we screen against. Naming one says we can test for it, not that we have delivered on it.
Evaluation and observability
Spans every layer. Without it a system is shipped on impressions.
- LangSmith
- Weights and Biases
Also screened against
- Langfuse
- MLflow
Experience and delivery
Copilots and agents inside a business process, and the interaction design that makes an uncertain system usable.
Orchestration and agents
Where an agent's steps, tools and state are defined, and where its failures are caught before a user meets them.
Also screened against
- Model Context Protocol
Models
The models themselves, and the platforms an enterprise hosts them through.
- Mistral
- Azure AI Foundry
- AWS Bedrock
- Google Vertex AI
- AWS SageMaker
- Hugging Face
Also screened against
- Meta (Llama)
Data and grounding
What the model is grounded in, and the integration work that gets enterprise data to where it can reach it.
- Databricks Mosaic AI
- Snowflake Cortex
- Microsoft Fabric
- Informatica IDMC
Systems you already run
The seven platform desks Yallo staffs. Almost no AI work is greenfield; it lands here.
Governance, risk and safety
Spans every layer. Named as what governance roles are screened against; what any of them obliges is your counsel's call.
- EU AI Act
- ISO/IEC 42001
- ISO/IEC 23894
- NIST AI Risk Management Framework
- OWASP Top 10 for LLM Applications
Seniority
What changes between mid, senior and lead.
The grade is a description of what the person owns, not a band. Rates come with the shortlist.
- Mid
- Runs and extends existing pipelines and dashboards. Executes a rollback under a runbook someone else wrote.
- Senior
- Owns the deployment and evaluation pipeline, defines what is monitored, and sets the thresholds that page a human.
- Lead
- Owns the operating model for AI in production across teams, including cost governance and the standard every service is held to before release.
In a programme
When this role is needed, and what blocks it.
Stood up early or paid for late. The pattern that hurts is treating this as a deployment-phase role: the evaluation harness and the rollback path have to exist before the first release, not after it. Retained permanently, because unlike a build role there is no point at which the work is finished.
Adjacent
The roles this one is confused with.
Ask
Send the brief, get a screened MLOps Engineer shortlist.
Tell us the programme, the stack and the timeline.