Vikaas · Hire ML engineers
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.
Training pipelines, feature stores, model serving, monitoring, and retraining — the operating layer that keeps a model alive after launch.
People who frame the business problem before the model and know when a simpler approach beats a heavier one.
Screened on systems that ran under real load and cost limits, not benchmarks tuned for a slide.
Direct hire, an engineer on your team, or a Banao-managed squad. We fit the model to the work, not the other way round.
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.
Banao (internal)
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.
Swiggy · Myntra
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.
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.
Sometimes the problem upstream of the model means a hire is the wrong first move.
Scope the work, then trial the person on it before you commit.
We separate the modelling work from the operating work, define the level you actually need, and set shortlist criteria. Yours to keep.
Interview-proven ML engineers, then a short paid trial on a real slice of your pipeline before a longer commitment.
The engineer joins under your chosen model, with one accountable contact at Banao, and the team scales as the work grows.
Do you screen for MLOps or just modelling?
Both, and the gap between them is the whole point. We screen for the operating skills — pipelines, serving, monitoring, retraining — that decide whether a model survives launch, not only for modelling ability that looks good in a notebook.
Can I hire a data scientist instead of an ML engineer?
Yes. The bench includes applied data scientists who frame the business problem and ML engineers who ship the system. The Hiring Discovery Sprint helps you decide which role your work actually needs — they are not interchangeable.
How is this different from hiring AI developers?
AI-developer hiring centres on generative-AI and LLM application work; ML-engineer hiring centres on training, serving, and operating models on your own data. If you are unsure which fits, the Sprint sorts it out before you commit.
How fast can an ML engineer start?
The bench is pre-screened, so a typical path is a one-week scoping Sprint, a shortlist in days, a short paid trial, then start — weeks, not a multi-month search.
Bring the model nobody can run or the pipeline nobody can hold. In 30 minutes we'll scope the hire that fixes it.
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