Hire vetted senior MLOps Engineers
24h
first response
72h
from brief to shortlist
1 in 7
clears our vetting
75/100
the pass mark
Hire senior MLOps and AI Infrastructure engineers through Mahala. Vetted production specialists in Kubernetes, model serving, CI/CD for models, and AI governance, matched to your stack in 72 hours. Every profile scored against our vetting protocol before it reaches your shortlist.
A model that drifts unmonitored keeps making yesterday's decisions on today's data. A serving platform without rollback turns every deployment into a bet. A regulator asks for the evaluation pipeline and there is nothing to show.
That is why we score MLOps engineers on operations evidence, not tool checklists. Anyone can list Kubernetes. The question is whether they have kept a model-serving platform alive under real load, handled the incident at 2am, and built the evaluation pipeline a regulator will accept.
What our MLOps Engineers deliver
01
Model-serving platforms: Kubernetes, KServe, Argo Workflows, containerised serving.
02
CI/CD for models: versioning, rollback, monitoring, rollout strategy, zero-downtime deployment.
03
Observability for AI systems: drift detection, data-quality alerting, model-decay monitoring, full-stack tracing.
04
AI governance: operational controls a regulator will accept, audit-grade evaluation pipelines.
When teams hire
through Mahala
Your model is only as reliable as the platform it runs on. The MLOps engineers we place here keep that platform alive in environments where downtime has compliance consequences.
Every MLOps engineer in our network has cleared two layers of vetting: production operations evidence first, then consulting capability. We assess how candidates handle incidents, governance, and the cross-functional work between data scientists, platform teams, and security. 75/100 is the gate, and six out of seven do not make it.
How an
engagement runs
- Brief us: role, stack, project phase, timeline, access model (VDI).
- We talk it through: a short call to sharpen the brief.
- Two or three blind CVs within 72 hours of a clear brief.
- Interview the finalists, choose the best fit.
- Mahala handles contracting, screening where required, and onboarding. One contract, one monthly invoice.
NDA on request. If your brief involves sensitive detail about the project, the team, or the IP, we offer a preliminary NDA as a service. Not required to receive a shortlist.
Representative
profile
Senior MLOps Engineer, six years across banking and insurance. Built the model-serving platform for a tier-one European bank: Kubernetes, KServe, Argo Workflows, evaluation pipelines, full audit trail for regulator submissions. Strong on AWS, GCP, Prometheus, Grafana, OpenTelemetry. Available remotely across the UK, Europe and the GCC, contracted via Mahala.ai. Vetted at 87/100.
Related
questions
What do Mahala MLOps engineers do?
Model-serving platforms on Kubernetes and KServe, CI/CD for models with versioning and rollback, observability for AI systems (drift, data-quality alerting, model-decay monitoring), and audit-grade evaluation pipelines. In short: the operational discipline that keeps a production model alive in an environment where downtime has compliance consequences.
How is MLOps different from DevOps or platform engineering?
DevOps and platform engineering keep code and infrastructure alive. MLOps adds the model-specific layer: data drift, model decay, evaluation, retraining schedules, and the governance that makes a model auditable. The overlap is real, but a general platform engineer will not spot silent model failure until it has already cost you.
How does Mahala vet MLOps Engineers?
Two layers of rigorous, evidence-based assessment. Layer one verifies production operations evidence: real incident handling, real rollback records, real evaluation pipelines a regulator has seen. Layer two assesses consulting capability: scoping, cross-functional work with data scientists and security, and the honesty to say what the platform can and cannot support. 75/100 is the pass mark, and one in seven applicants clears it.
Do they work in regulated environments such as VDI, Citrix, or Azure Virtual Desktop?
Yes. Most of our enterprise placements operate in regulated remote environments. We confirm access requirements in the brief and only send specialists with the operational discipline for regulated remote work.
What tools do your MLOps engineers use?
Kubernetes, KServe, containerised model serving, Argo Workflows, MLflow, Prometheus, Grafana, OpenTelemetry, and the cloud-native stacks on AWS, GCP, and Azure. Tooling on its own is not the point. The point is documented delivery on those tools under real load, with the incident record to back it up.
How is it priced?
Senior delivery is priced on a day or hourly rate, depending on the engagement model. We share rate ranges during the first call.