AI & ML · India

Hire Machine Learning Engineers

End to end ML engineers covering model training, MLOps, and production deployment with Python, PyTorch / TensorFlow, and modern feature stores.

Hire a Machine Learning Engineer See All 46 Roles

Skill Set

What our Machine Learning Engineers know cold

Every engineer you hire from RG INSYS is benchmarked on these areas before they touch your codebase.

Modeling

PyTorch, TensorFlow / Keras
scikit-learn, XGBoost, LightGBM
Hugging Face Transformers
Time series: Prophet, Temporal Fusion, Darts

Data & Features

Pandas, Polars, NumPy
Feature stores: Feast, Tecton
Spark, Dask for distributed training
Labeling: Label Studio, Argilla

MLOps

MLflow, Weights & Biases, Comet
DVC for data versioning
Model registries and CI for ML
Drift detection (Evidently, WhyLabs)

Deployment

AWS SageMaker, GCP Vertex AI, Azure ML
TorchServe, TF Serving, Triton Inference Server
ONNX runtime and quantization
GPU autoscaling on Kubernetes

What They Build

Real work delivered, not hypothetical

Production classifiers for fraud, churn, lead scoring, and propensity
Recommender systems for ecommerce, content, and B2B SaaS
Forecasting models for demand, revenue, capacity, and ops
Computer vision and NLP services exposed as REST/gRPC APIs
End to end MLOps pipelines with retraining and shadow deployments
Hybrid LLM + traditional ML stacks (LLM for understanding, ML for scoring)

Sample Engagements

Recent Machine Learning Engineer work at RG INSYS

Built a fraud detection ML pipeline for a UK fintech catching 38% more fraud at 0.8% false positive rate.

Shipped a SageMaker hosted recommender for a retail client lifting conversion 14% in A/B test.

Delivered an MLflow + Vertex AI pipeline retraining 9 customer models nightly with full lineage and rollback.

How hiring works

From brief to working Machine Learning Engineer in five steps

You choose who joins your team. We handle sourcing, contracts and backup cover.

01
Share your requirements
Tell us the stack, seniority, timezone and start date. We sign an NDA before you share anything sensitive.
02
Get a shortlist in 48 hours
We send 2 to 3 Machine Learning Engineer profiles matched to your brief within 48 hours of the signed NDA.
03
Interview and choose
Run your own technical interview or code exercise, then pick the engineer you want.
04
Start in 5 to 10 working days
Your engineer joins your Slack, Jira and GitHub with at least 4 hours of overlap with your team.
05
2 week free trial
Pay nothing for the trial if you do not continue. Not the right fit? We replace the engineer at no cost.

Scope growing? Add frontend, backend, DevOps or QA engineers under one tech lead with the Dedicated Team model, without re-explaining the project. Start with step 1.

Engagement Models

Three ways to hire your Machine Learning Engineer

Switch models anytime. No long term lock in. 2 week free trial available.

Hourly

From $55/hr

For short tasks, code reviews, urgent fixes, or proofs of concept.

Minimum 20 hours/week
Weekly time tracking and reporting
Cancel anytime with 1 week notice

A Machine Learning Engineer who codes with AI from day one

Hiring a Machine Learning Engineer from RG INSYS is different from a typical staffing engagement. Every engineer on our bench uses Claude Code, Cursor IDE, and GitHub Copilot daily. That is not a sales line, it is a hard requirement on day one of the contract.

For you that means three things. Faster shipping: the AI handles the boilerplate so the engineer focuses on architecture, edge cases, and your domain. Lower cost: you get senior India based engineers at the rate of UK or US juniors. Higher quality: AI generated tests run on every PR before human review, and our internal benchmarks show roughly 2.5× to 3× output uplift on production codebases.

If the engineer is not the right fit in the first 2 weeks you pay nothing for the trial period and we replace them at no cost. NDAs are signed before any disclosure and full IP is assigned to you, the code lives in your repos under your accounts.

FAQ

Common questions about hiring a Machine Learning Engineer

How quickly can a Machine Learning Engineer start?

For most Machine Learning Engineer roles we send a 2 to 3 person shortlist within 48 hours of a signed NDA. The chosen engineer typically starts within 5 to 10 working days, depending on your interview pace.

What level of seniority do you provide?

Our default is mid to senior (4+ years of production experience). We also have principal engineers for architecture work and tech leads available on request, both at higher monthly rates.

Can the Machine Learning Engineer work in our timezone?

Yes. Engineers commit to a flexible 8 hour day with at least 4 hours overlap with your team. UK, EU, US East/West, UAE, and Australia are all supported.

Do you replace the engineer if it's not a fit?

Yes. The first 2 weeks are a free trial. If you decide not to continue, you pay nothing for the trial and we replace the engineer at no cost.

Who owns the IP and the code?

You do. Every engagement includes a full IP assignment clause and an NDA. The code lives in your repos, on your infrastructure, under your accounts. We do not retain copies after handover.

What happens if the engineer is on leave or sick?

Planned leave is communicated 2 weeks in advance and is covered by a backup engineer briefed by the primary. Unplanned sickness is handled the same way. You never pay for time the engineer isn't producing.

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Free shortlist within 48 hours

Ready to hire your
Machine Learning Engineer?

Tell us the role, stack, and timezone. We'll send a 2 to 3 person shortlist with CVs, GitHub, and rates within 48 hours. Free 2 week trial available.

Hire a Machine Learning Engineer

Typical response within 4 business hours · NDA on request before any disclosure