Hire vetted senior
Machine Learning Engineers
24h
first response
72h
from brief to shortlist
1 in 7
clears our vetting
75/100
the pass mark
Hire senior Machine Learning (ML) Engineers through Mahala. Vetted production specialists in machine learning systems, feature pipelines, training, serving, and monitoring, matched to your stack in 72 hours. Every profile scored against our vetting protocol before it reaches your shortlist.
A wrong ML hire builds a model that ships fine, drifts silently, and takes six months to un-ship. The model runs, the dashboard renders, and the business trusts a system that has quietly stopped working. That is why we score ML Engineers on production evidence, not portfolio flash. Anyone can train a model in a notebook. The question is whether they have kept one alive in production with monitoring, retraining, and the operational discipline the environment demands.
What our ML Engineers deliver
01
Production ML systems: feature pipelines, training, serving, monitoring.
02
Retraining and evaluation frameworks that survive real data drift.
03
Model observability, tracing, and rollout strategy inside regulated environments.
04
The engineering discipline to move ML from notebook to production and keep it there.
When teams hire
through Mahala
Most ML hires fail on the second month, not the first. The model works in staging, ships to production, and then quietly rots because nobody built the drift detection or the retraining pipeline. The engineers who close that gap are not the engineers who built the proof-of-concept. They are who Mahala vets for.
Every ML Engineer in our network has cleared two layers of vetting: production delivery evidence first, then consulting capability. We assess how candidates approach evaluation, drift, and the operational realities of running ML systems at scale. 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 ML Engineer, seven years across financial services and healthcare. Built and operates a production ML platform serving 40+ models with drift detection, automated retraining, and full audit trail for regulator submissions. Strong on Python, PyTorch, MLflow, Airflow, Kubernetes. Available remotely across the UK, Europe and the GCC, contracted via Mahala.ai. Vetted at 88/100.
Related
questions
What do Mahala ML Engineers do?
Build and deploy production ML systems: feature pipelines, training, serving, monitoring. Design retraining and evaluation frameworks that survive real data drift. Take responsibility for the long tail of post-launch model behaviour.
What is the difference between an ML Engineer and an AI Engineer?
ML Engineers own the production ML system: training, serving, monitoring, retraining. AI Engineers focus on the model itself and the application layer, including GenAI, retrieval, and evaluation frameworks. There is real overlap, but the operational responsibility is what separates the two. For most enterprise production work, ML Engineer is the correct hire.
How does Mahala vet ML Engineers?
Two layers of rigorous, evidence-based assessment. Production delivery evidence first, then consulting capability. We ask candidates how they handle drift, retraining, and post-launch model behaviour. 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. Standard for our placements. We confirm access requirements in the brief and only send specialists with the operational discipline for regulated remote work.
What tools do your ML Engineers use?
Python, PyTorch, TensorFlow, MLflow, Airflow, Kubernetes, containerised serving, 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.
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.