AI and ML Engineering,
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 AI/ML Engineers into enterprise and government teams shipping production systems, beyond the proof-of-concepts. Every profile scored against our vetting protocol before it reaches your shortlist.
What a wrong AI/ML hire costs
Every AI demo works. That is what makes this discipline dangerous to hire in. The gap between a convincing prototype and a production system: evaluation frameworks, guardrails, edge cases, observability, the operational duty of running a model in front of real users, is exactly where most AI projects die, and exactly what a demo never shows you.
That is why we score AI/ML Engineers on production evidence, not portfolio flash. Anyone can wire a RAG demo in a weekend. The question is whether they have run one in production with the evaluation to know when it is wrong, the guardrails for when it misbehaves, and the uptime record to prove it.
What our AI/ML Engineers deliver
01
Generative AI in production: retrieval architectures, evaluation frameworks, guardrails, observability.
02
Production ML systems: feature pipelines, training, serving, monitoring.
03
Computer vision and forecasting systems with enterprise-grade rigor.
04
AI delivery in healthcare, finance, and regulated environments, inside your VDI, under your policies.
When teams
choose Mahala
Our engineers do it all: design, deployment, monitoring, evaluation, and the operational responsibility of a model with real users.
Every AI/ML Engineer on the bench has cleared our vetting protocol: scored on evidence across technical depth, demonstrated impact, consulting aptitude, and trajectory. We ask how they approach evaluation, edge cases, and the unglamorous operational realities of ML at scale, because that is where senior lives. 75/100 is the gate. Six out of seven don't 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.
Representative
profile
Senior AI/ML Engineer. Primary counterpart at his last engagement: the CEO. He surfaced the opportunities, proposed the solutions, and owned technical delivery end to end in a regulated healthcare environment, cutting manual processing by 40%. Deployed retrieval-augmented systems across 200+ healthcare facilities at 99.2% uptime. Runs the full modern stack: LangChain, vector databases, multi-LLM routing, Terraform, Kubernetes, end-to-end observability. Vetted at 91/100.
Related
questions
What do your AI and ML Engineers deliver?
Production ML systems, GenAI applications with retrieval and guardrails, computer vision and forecasting in production, and the model-evaluation work that separates a launched system from a demo.
How do you vet AI / ML Engineers?
Two layers of institutional-grade assessment, scored on production delivery evidence first, then consulting capability. Hackathon and proof-of-concept work do not clear the bar.
Do your engineers work on GenAI specifically?
Yes. Senior practitioners with documented delivery in retrieval, evaluation frameworks, guardrails, and observability for GenAI applications in regulated production environments.
Can they work in VDI or Citrix environments?
Yes. Standard for our placements. We confirm access requirements in the brief.
What's the typical engagement length?
Most engagements run 3 to 12 months, with extended retainers for multi-year programmes.