ML engineers measured on what they shipped, not what they trained
A model that scores well in a notebook is not a hire who can run it in production. Vikaas gives you ML and MLOps engineers screened on real systems — pipelines, serving, monitoring, drift — from the pool Banao staffs its own work from.
You interview people who have taken a model from data to a live endpoint that holds up under load, not candidates who stop at a Kaggle leaderboard.
Banao — runs its own ML and data workloads on engineers hired through this pipeline.
ML hiring fails on the gap between modelling and operating. We screen for both, so you are not left with a prototype nobody can run.
ML & MLOps engineers
Training pipelines, feature stores, model serving, monitoring, and retraining — the operating layer that keeps a model alive after launch.
Applied data scientists
People who frame the business problem before the model and know when a simpler approach beats a heavier one.
Production track record
Screened on systems that ran under real load and cost limits, not benchmarks tuned for a slide.
Your engagement model
Direct hire, an engineer on your team, or a Banao-managed squad. We fit the model to the work, not the other way round.
Our own ML workloads run on this bench
Banao operates its own ML and data products with engineers hired through Vikaas. We feel the cost of a hire who can model but not operate, which is exactly what the screen is built to catch.
Data-heavy engineering delivered by bench teams
Banao has delivered data-heavy engineering for names like Swiggy and Myntra with teams from its own bench — the same supply chain behind these ML hires.
Metrics shown dotted (··) are being finalised in our case-study metrics pack. The systems are live; we will not publish a number before it is verified.
Our own ML runs on people from this pool
Banao operates a ~300-person engineering company and staffs its own ML and data work from the same bench you hire from. When a hire can build a model but not keep it running, our own products are the first to feel it.
That is the difference from a vendor forwarding ML résumés it never had to depend on. The screen exists because we live with the result.
Vikaas
Sources the ML engineers Banao hires for its own data work.
InterviewGod
Screens for production ability, not benchmark scores.
When an ML hire isn't your real bottleneck
Sometimes the problem upstream of the model means a hire is the wrong first move.
No usable data yet
hire data engineering first; an ML engineer with nothing to train on will idle.
A one-off model
a fixed-scope build may cost less than a permanent hire you have to keep busy.
Unclear objective
if success isn't defined, start with an AI Discovery Sprint to frame it before hiring.
Scope the work, then trial the person on it before you commit.
Hiring Discovery Sprint
We separate the modelling work from the operating work, define the level you actually need, and set shortlist criteria. Yours to keep.
Shortlist & paid trial
Interview-proven ML engineers, then a short paid trial on a real slice of your pipeline before a longer commitment.
Hire & scale
The engineer joins under your chosen model, with one accountable contact at Banao, and the team scales as the work grows.
The questions buyers ask first.
Do you screen for MLOps or just modelling?
Can I hire a data scientist instead of an ML engineer?
How is this different from hiring AI developers?
How fast can an ML engineer start?
Tell us the ML work that's stalled
Bring the model nobody can run or the pipeline nobody can hold. In 30 minutes we'll scope the hire that fixes it.
Book a hiring call →