TensorFlow development company in India
TensorFlow models trained, deployed, and monitored by an India-based team inside your own cloud account, with ML, data, and MLOps roles staffed separately and data protection terms agreed before anyone sees a record.
- India-based senior engineers
- IST + US/UK overlap
- 24-hour reply
- Projects shipped
- 50+
- Code ownership
- 100%
- Client rating
- 4.8/5
- First reply
- 24h
TensorFlow engineering roles in India
Krapton is set up as a TensorFlow development company in India with separate people for modeling, data pipelines, and serving, because one generalist rarely covers all three well. An ML engineer designs and trains Keras models; a data engineer builds tf.data or TFX pipelines and validates incoming data; and an MLOps engineer handles TensorFlow Serving, CI for models, and monitoring. A mobile engineer joins when models ship through LiteRT. Long training runs and evaluation jobs execute during Indian hours, and results arrive as TensorBoard links and written summaries for review in your overlap window. Data stays in your cloud project: engineers work through IAM roles you grant and notebooks hosted there, nothing is copied to personal machines, and personal data is minimized or pseudonymized before training. Data processing terms are signed alongside the NDA, covering GDPR transfer mechanisms or India’s Digital Personal Data Protection Act where they apply.
TensorFlow development services in India
ML engineers for your team
India-based engineers who join your data science team full-time, write training code in Keras, run experiments in your tracking tool, and present results during shared hours.
Data pipeline engineering
Data engineers who build tf.data and TFX ingestion, validate schemas with TensorFlow Data Validation, and keep feature logic consistent between training jobs and serving code.
MLOps and serving team
Engineers who package models for TensorFlow Serving or Vertex AI, automate retraining pipelines, add drift and latency monitoring, and manage rollbacks through model version labels.
On-device ML engineers
Mobile-focused engineers who convert models to LiteRT, apply quantization, benchmark them on target Android and iOS devices, and integrate inference into your existing apps.
Model audit and rescue
A senior ML engineer who reviews an inherited TensorFlow project for data leakage, skew, unreproducible training, and weak evaluation before recommending whether to fix or retrain.
Framework and driver upkeep
Ongoing upkeep: TensorFlow and Keras upgrades tested against stored predictions, CUDA and driver compatibility checks, and scheduled retraining runs with results reported in shared hours.
Why an India-based TensorFlow team from Krapton
Senior engineers, fixed scope, and code you own — the things agencies promise and rarely deliver.
Your data stays in your cloud
Training data, checkpoints, and models remain in your storage buckets and projects, accessed through roles you grant and revoke, so nothing sensitive sits on vendor infrastructure.
Vetting focused on ML failures
Candidates diagnose a leaking validation split, a retracing tf.function, and an input pipeline starving the GPU, which shows practical judgment that a framework quiz cannot.
Reproducibility as a review gate
Pull requests that change training code must record seeds, dataset versions, and package versions, and reviewers rerun key experiments before results are accepted.
Training time used well
Long training and evaluation jobs run through the Indian working day, so your team reviews finished results during overlap rather than waiting on runs.
How we run TensorFlow projects from India
01
Roles and data access
The ML, data, MLOps, and mobile roles the work needs are agreed first, together with a data access model and privacy terms, before any engineer is onboarded.
02
Cloud environment setup
Engineers receive IAM roles, hosted notebooks, and tracking tools inside your project, with logging enabled so your security team can audit data access.
03
Experiment and review cycles
Each cycle ends with an experiment report comparing candidates against the baseline, and model choices are agreed with your team during the overlap window.
04
Deploy, then decide ownership
The selected model ships with serving configuration, monitoring, a retraining runbook, and a model card, then ownership moves to your team or stays with ours.
TensorFlow tools our India team works with
TensorFlow development in India: questions answered
How does a TensorFlow development company in India protect our training data?
By keeping data in your environment and limiting who can touch it. Engineers work inside your cloud project through IAM roles you control, datasets are minimized or pseudonymized where possible, and nothing is downloaded to personal devices. For EU or UK personal data, the contract includes standard contractual clauses or the UK equivalent, and any obligations under India’s Digital Personal Data Protection Act are addressed as well.
What exercises reveal real TensorFlow experience?
Problems that expose production judgment. Ask candidates to find the leak in a train and validation split, explain why a tf.function keeps retracing, speed up a tf.data pipeline that leaves the GPU idle, and describe how they would detect training-serving skew. Strong engineers also question the metric itself. Shortlisted engineers can go through the same exercises with your team before joining.
Can we bring in MLOps help without changing our data science team?
Yes, MLOps can be added on its own. Plenty of companies have models but no reliable way to serve, monitor, or retrain them. An India-based MLOps engineer can package existing models for TensorFlow Serving or Vertex AI, build CI that tests models before deployment, add drift and latency monitoring, and automate retraining, while your data scientists keep owning the modeling work.
Who should be on a TensorFlow team staffed from India?
It follows the bottleneck. A modeling-heavy project starts with ML engineers and a data engineer; a project that already has models but struggles to ship them starts with MLOps; and on-device work adds a mobile engineer for LiteRT integration. A senior ML engineer stays accountable for methodology throughout and reviews experiments before they are presented to you.
How are results reviewed when training runs overnight for us?
Through written experiment reports and a short live review. When your team starts its day, each completed run has metrics compared with the baseline, TensorBoard links, notes on what changed, and a recommendation. Decisions on which model to keep, what to try next, or whether to stop are made together during the overlap window, and every decision is logged with the experiment.
Ready to build? Tell us what you have in mind.
Send a short brief and a senior engineer will reply within one business day with next steps — no sales fluff.