Hire MLOps Engineer — models that survive production

Data engineering builds pipelines and warehouses that move data; MLOps deploys models and keeps them healthy with monitoring, retraining, and safe releases. Most production AI needs both, in that order.

A first model behind a monitored endpoint typically ships in three to four weeks. Full CI/CD with feature store, drift monitoring, and retraining loops usually takes eight to twelve weeks.

Both. Classical models get feature stores and batch or real-time serving; LLM apps get eval harnesses, prompt versioning, and cost/latency guardrails. The deployment discipline is the same either way.

Input drift when your data changes, prediction drift when outputs shift, and performance drops against business metrics. Alerts fire with enough context to decide: retrain, roll back, or investigate.

Yes — I start with an audit of your training and serving setup, stabilize the riskiest parts first, then modernize incrementally. No rip-and-replace unless the current stack is genuinely unsalvageable.

I work on a fixed-scope project fee or a fractional monthly retainer, never hourly. For a single model taken to production I quote a fixed price against a clear definition of done; for ongoing platform work I reserve a set number of days a month. When you hire an MLOps engineer as a contractor rather than a salaried headcount, you pay for senior judgment only while you need it. I give a firm number after the initial audit, so there are no surprises later.

I work inside whatever you already run: SageMaker on AWS, Vertex AI on GCP, Azure ML, or a plain Kubernetes cluster. I have no appetite for a rip-and-replace that leaves you with an unfamiliar system. My job is to make your existing platform reliable and reproducible, adding open tools like MLflow, Evidently or KServe only where they close a real gap, never because I happen to prefer them.

My focus is the engineering around the model: serving, pipelines, monitoring, evaluation and cost. I am comfortable reading and refactoring model code, improving training reproducibility and advising data scientists, but I am not a substitute for a dedicated research team on novel modelling. In practice most teams hire an MLOps engineer precisely because their data scientists are strong at modelling and need someone to make that work survive contact with production.

If you have a steady stream of models and a mature platform, hire full-time; the role pays for itself. Most teams I meet are earlier than that. They need the platform built correctly once and their existing engineers levelled up, which is a months-long job rather than a permanent one. I usually build the foundation and the runbooks, then hand over to an internal hire who maintains it, which is far cheaper than a senior salary paid before the work exists to justify it.

Yes. I have worked where data cannot leave the premises and every dependency must be mirrored internally. The principles are unchanged, reproducibility, versioning and monitoring, but the execution leans on self-hosted registries, private container mirrors and strict lineage for audit. If you are in finance or healthcare and need model decisions to be explainable and traceable after the fact, that constraint shapes the architecture from day one rather than being bolted on at the end.

Everything I build is yours and documented: infrastructure as code, runbooks for retraining and incidents, and pairing sessions so your engineers understand every decision rather than inheriting a black box. I deliberately avoid bespoke glue that only I can maintain, favouring standard, well-supported tools. A good engagement ends with your team confidently running the platform without me on call. If I have done it right, you should not need to hire an MLOps engineer again for the same problem.