MLOps and AI Infrastructure,
delivered by vetted seniors
24h
first response
72h
from brief to shortlist
1 in 7
clears our vetting
75/100
the pass mark
Mahala places senior MLOps and AI Infrastructure engineers into enterprise and government teams running production AI systems. Every profile scored against our vetting protocol before it reaches your shortlist.
What a wrong MLOps hire costs
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.
That is why we score platform 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 engineers deliver
01
Model-serving platforms
Kubernetes, containerised serving, workflow orchestration.
02
CI/CD for models
Versioning, rollback, 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 choose Mahala for MLOps
Your model is only as reliable as the platform it runs on. The MLOps engineers in Mahala's network are the people who 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 engineers, 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.
- We match from our two-layer vetted bench.
- Two to three blind CVs in 72 hours.
- Interview the finalists, choose the best fit.
- Mahala handles contracting, compliance, and onboarding.
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 Europe and the GCC, contracted via Mahala.ai. Vetted at 87/100.
Related
questions
What do Mahala MLOps engineers deliver?
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 or Citrix?
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 long do MLOps engagements usually run?
Most run 3 to 12 months, with extended retainers for multi-year platform programmes. Shortlist arrives within 72 hours of a clear brief, and most placements are working in your environment within two weeks.