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Hire expert TensorFlow developers

Add a TensorFlow engineer who takes models from experiment to production: Keras training code, efficient tf.data input pipelines, SavedModel exports, TF Serving or Vertex AI endpoints, and LiteRT builds for mobile and edge.

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A cleaner way to hire TensorFlow developers

Krapton helps you hire TensorFlow developers who maintain and extend production machine learning systems built on TensorFlow and Keras: image and text classifiers, recommendation and ranking models, time-series forecasts, and on-device...

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24h
Replacement
30d
IP yours
100%

  • Input pipelines that feed GPUs

    Many slow training jobs are input-bound, not compute-bound. Developers build tf.data pipelines with parallel map, prefetch, caching and TFRecord sharding, read the TensorBoard Profiler’s input-pipeline analysis, and keep accelerators busy instead of waiting on Python preprocessing.

  • No training-serving skew

    Preprocessing that differs between training and serving silently ruins accuracy. Our engineers put normalization and tokenization inside the model with Keras preprocessing layers or TensorFlow Transform, so the exported SavedModel applies identical logic everywhere it runs.

  • Graph mode understood

    tf.function makes code fast but brings retracing, Python side effects and shape surprises. Experienced developers set input signatures, avoid Python control flow on tensors where AutoGraph cannot convert it, and enable XLA compilation only after measuring that it helps.

  • Edge deployment that fits

    Getting a model onto a phone or microcontroller means conversion, quantization and operator support. Developers convert to LiteRT, apply post-training quantization or quantization-aware training, check that every op is supported by the target delegate, and benchmark latency on real devices.

Why TensorFlow?

What makes TensorFlow the right choice for modern engineering teams.

  • Deep learning

    Tensorflow supports deep learning models, allowing developers to build and train neural networks for complex tasks.

  • Versatile model deployment

    Tensorflow enables the deployment of machine learning models on diverse platforms, including mobile devices and the cloud.

  • Extensive library ecosystem

    Tensorflow offers a rich ecosystem of libraries and tools for tasks such as computer vision, natural language processing, and reinforcement learning.

  • Tensorboard visualization

    Tensorboard provides a powerful visualization tool for monitoring and analyzing machine learning model performance and training progress.

  • Scalability

    Tensorflow is designed for scalability, making it suitable for both small-scale and large-scale machine learning projects.

  • Community and support

    With a large and active community, Tensorflow ensures continuous improvement, support, and knowledge sharing among developers.

What you can build with TensorFlow

  • Computer vision models

    Image classification, object detection and segmentation built on pretrained backbones such as EfficientNet or MobileNet, fine-tuned with Keras, and exported for server inference or on-device use.

  • Recommendation systems

    Two-tower retrieval and ranking models with TensorFlow Recommenders, embedding tables sized for large catalogs, and offline evaluation with recall and NDCG before an online A/B test.

  • Forecasting and anomaly detection

    Demand and load forecasting plus anomaly detection with sequence models, compared against gradient-boosted baselines, using windowed tf.data datasets and backtesting so accuracy is judged on periods the model never saw.

  • On-device ML features

    Offline features in Android and iOS apps, such as document scanning, keyword spotting or pose detection, running through LiteRT with GPU delegates and small quantized models.

  • Browser inference

    Models running client-side with TensorFlow.js on the WebGL or WebGPU backends, for privacy-preserving features such as background blur or gesture input, with instant feedback and no server round trip.

  • Production ML pipelines

    End-to-end training pipelines with TFX or Vertex AI Pipelines covering data validation, transformation, training, evaluation against a baseline, and a push step that only promotes models passing defined thresholds.

Taking TensorFlow models from notebook to production

A model that scores well in a notebook is often a long way from one that can be trusted in production. Krapton’s TensorFlow developers close that gap in a predictable order. Training code moves out of notebooks into versioned modules with fixed random seeds, pinned TensorFlow, CUDA and cuDNN versions, and configuration files instead of edited cells. Preprocessing is folded into the model graph so the exported SavedModel carries its own normalization and vocabulary. Evaluation runs on a held-out set that reflects real traffic, sliced by the segments the business cares about, and the result is compared with the current production model rather than with an earlier experiment. Only a candidate that beats the baseline on agreed metrics gets promoted, and every promoted model is traceable back to the exact data snapshot and code commit that produced it.

Serving is designed around the latency and cost profile of the product. For server inference, TF Serving or a Vertex AI endpoint runs the SavedModel with request batching tuned against a latency budget, and the client calls a stable signature so models can be swapped without code changes. For mobile and edge, the developer converts to LiteRT, checks operator coverage early, and quantizes with a representative dataset, measuring the accuracy drop instead of assuming it is acceptable. After release, the work continues: input distributions and prediction confidence are logged and compared against training data, alerts fire on drift, and a retraining pipeline can be triggered with the same validation gates. Your team ends up with a model, the pipeline that rebuilds it, and the documentation needed to keep improving it without depending on the person who trained the first version.

TensorFlow in action

import tensorflow as tf
        from tensorflow.keras import layers, models
        from tensorflow.keras.datasets import mnist
        from tensorflow.keras.utils import to_categorical
        
        # Load and preprocess the MNIST dataset
        (train_images, train_labels), (test_images, test_labels) = mnist.load_data()
        
        # Normalize pixel values to be between 0 and 1
        train_images, test_images = train_images / 255.0, test_images / 255.0
        
        # One-hot encode the labels
        train_labels = to_categorical(train_labels)
        test_labels = to_categorical(test_labels)
        
        # Build a simple convolutional neural network (CNN) model
        model = models.Sequential([
            layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
            layers.MaxPooling2D((2, 2)),
            layers.Conv2D(64, (3, 3), activation='relu'),
            layers.MaxPooling2D((2, 2)),
            layers.Conv2D(64, (3, 3), activation='relu'),
            layers.Flatten(),
            layers.Dense(64, activation='relu'),
            layers.Dense(10, activation='softmax')
        ])
        
        # Compile the model
        model.compile(optimizer='adam',
                      loss='categorical_crossentropy',
                      metrics=['accuracy'])
        
        # Reshape the input data for the CNN
        train_images = train_images.reshape((60000, 28, 28, 1))
        test_images = test_images.reshape((10000, 28, 28, 1))
        
        # Train the model
        model.fit(train_images, train_labels, epochs=5, validation_data=(test_images, test_labels))
        
        # Evaluate the model
        test_loss, test_acc = model.evaluate(test_images, test_labels)
        print(f'Test accuracy: {test_acc}')

What our TensorFlow developers know

Every Krapton developer is vetted with real production experience in TensorFlow across multiple industry domains.

Hire a developerSchedule a call
  • Tensorflow proficiencyExpertise in using Tensorflow for developing and implementing machine learning models.
  • Deep learning knowledgeMastery of deep learning concepts and techniques for building sophisticated neural network architectures.
  • Model deploymentSkills in deploying machine learning models on various platforms, ensuring widespread accessibility.
  • Library utilizationProficient in utilizing Tensorflow's extensive library ecosystem for specific machine learning tasks.
  • Tensorboard usageAbility to use Tensorboard for visualizing and optimizing machine learning model performance.
  • Scalable solutionsExperience in designing scalable machine learning solutions with Tensorflow for different project scales.

What our TensorFlow developers go deep on

  • Keras model development

    Functional and subclassed Keras models, custom layers and losses, callbacks for checkpointing and early stopping, and awareness of Keras 3 changes such as the .keras save format and backend-agnostic ops.

  • Distributed and accelerated training

    MirroredStrategy and MultiWorkerMirroredStrategy for GPUs, TPUStrategy on Cloud TPU, mixed precision policies, and gradient accumulation or larger batches tuned with learning-rate schedules that keep convergence stable.

  • Model serving and versioning

    SavedModel exports with explicit serving signatures, TF Serving with model version policies and batching, gRPC and REST clients, and canary rollout of new versions with rollback when metrics regress.

  • Data validation and MLOps

    TensorFlow Data Validation for schema and skew checks, experiment tracking with MLflow or Vertex AI, reproducible environments pinned by CUDA and cuDNN version, and CI that runs a short smoke-training job on every change.

  • Model quality and fairness checks

    Evaluation sliced by segment with TensorFlow Model Analysis, calibration checks, confusion matrices per class, and documented failure cases, so stakeholders see where a model is weak before it ships.

  • Security of ML artifacts

    Loading only trusted SavedModels and .keras files, since models can contain executable graph operations or Lambda layers, keeping safe_mode enabled in Keras, and scanning dependencies and container images for vulnerabilities.

Common TensorFlow projects we deliver

  • Moving a TensorFlow 1.x model to TensorFlow 2

    Legacy code built on sessions, placeholders and tf.compat.v1 is rewritten in eager-mode Keras or tf.function, checkpoints are converted, and outputs are compared layer by layer so accuracy matches before the old graph is retired.

  • Speeding up a slow TensorFlow training job

    A developer profiles the run with the TensorBoard Profiler, fixes input bottlenecks, enables mixed precision on supported GPUs, tunes batch size, and spreads training across devices with tf.distribute strategies when a single accelerator is the limit.

  • Shrinking a model for a mobile release

    The model is pruned or distilled where needed, converted to LiteRT, quantized to int8 with a representative dataset, and validated on target phones for latency, memory and accuracy against the full-precision version.

  • Putting a notebook model into production

    Research code from a notebook becomes a versioned training script, a SavedModel with a defined serving signature, a TF Serving or Vertex AI endpoint behind autoscaling, and monitoring for prediction drift and latency.

TensorFlow solutions by industry

  • Retail and ecommerce

    Product recommendations, search ranking, demand forecasting and visual search, where models are retrained on fresh catalog and behavior data on a regular schedule.

  • Healthcare

    Medical image classification and triage support, built with careful validation splits by patient, explainability checks and documentation suited to clinical and regulatory review.

  • Manufacturing

    Visual defect detection on production lines and predictive maintenance from sensor data, often deployed to edge devices next to the cameras and machines.

  • Agriculture

    Crop disease detection and yield estimation from drone or phone images, using on-device models that work in fields without reliable connectivity.

TensorFlow vs PyTorch

PyTorch now dominates research and most new generative AI work, and many teams starting fresh choose it. TensorFlow remains strong where deployment tooling matters: TF Serving, LiteRT for mobile and microcontrollers, TensorFlow.js and TPUs. Keras 3 can run on TensorFlow, PyTorch or JAX, which softens the choice. If you already have TensorFlow models in production, keeping and improving them is usually wiser than a rewrite.

TensorFlow vs PyTorch
DimensionTensorFlowPyTorch
Research adoptionDeclining share of new papers and modelsDefault for most new research and LLMs
Mobile and edgeLiteRT with mature converters and delegatesExecuTorch, newer but growing quickly
ServingTF Serving with SavedModel signaturesTriton, vLLM or custom FastAPI servers
Browser inferenceTensorFlow.js runs models directly in browsersUsually via ONNX Runtime Web export
AcceleratorsFirst-class TPU support, plus NVIDIA GPUsNVIDIA GPUs first, TPUs through PyTorch/XLA
Execution modelEager by default, graphs via tf.functionEager by default, compile via torch.compile

How to hire TensorFlow developers in 5 steps

A transparent, low-risk process — from first call to onboarded engineer in about two business days.

  1. 01Day 0

    Share your brief

    Tell us the stack, scope, seniority and the timezone overlap you need on a free 30-minute discovery call. No obligation, no sales pitch.

  2. 02Within 24 hours

    Review matched profiles

    We shortlist 2–3 pre-vetted TensorFlow engineers from our in-house bench, with CVs and directly relevant work — usually within one business day.

  3. 03Days 1–2

    Interview & test

    Interview each developer yourself, run a paid trial task, or do a live pairing session. You decide who joins — we never push a profile.

  4. 04Within 2 days

    Onboard securely

    Sign a free NDA, then plug the developer into your Jira, repository and Slack. Engagements are month-to-month with no lock-in.

  5. 05Ongoing

    Scale or replace

    Scale the team up or down month by month as the roadmap changes. If the fit is not right, you get a free replacement within the first 30 days.

More AI / ML technologies

Other AI / ML technologies we work with at Krapton.

Three ways to hire TensorFlow developers

Pick the engagement that matches how you actually work. No multi-year contracts — scale up or down month by month.

  • Dedicated developer

    Most popular

    Full-time TensorFlow engineer who reports only to you. Best for ongoing products, long-term roadmaps and teams that need a core hire without the HR overhead.

    • 40 hours / week
    • Your Jira, your repo
    • Month-to-month

  • Hourly / time & materials

    Pay only for billable hours. Ideal for research spikes, code audits, or variable-load TensorFlow work where scope is still being discovered.

    • Weekly timesheets
    • Slack-first comms
    • No minimum commit

  • Fixed-price milestones

    Scoped delivery with clear milestones and acceptance criteria. Best for well-defined TensorFlow builds like an MVP, a migration or a specific module.

    • Scope locked upfront
    • Milestone acceptance
    • Predictable budget

Compare ways to hire TensorFlow developers

Pick the model that matches how you work. Switch between them as your TensorFlow project evolves — no multi-year contracts.

Compare ways to hire TensorFlow developers
EngagementDedicated developer (popular)Hourly / time & materialsFixed-price milestones
Best forOngoing products & long-term roadmapsExploratory or variable-load workWell-scoped MVPs, modules & migrations
BillingMonthly, full-time (~160 hrs)Per billable hourPer accepted milestone
CommitmentMonth-to-month, no lock-inNo minimum commitmentScope locked up front
FlexibilityScale up or down monthlyHighest — pay for what you useLowest — defined deliverables
You manageDay-to-day in your toolsWeekly prioritiesAcceptance criteria only
ReportingDaily standupsWeekly timesheetsMilestone demos

Hire TensorFlow developers from anywhere

Our New Delhi team partners with founders and engineering leaders across eight key markets, with enough daily timezone overlap to feel in-house.

  • Hire TensorFlow developers in India

    Full working-day overlap

    Krapton is an on-shore TensorFlow studio in New Delhi. Hire TensorFlow developers in India and get senior, full-time engineers at roughly 50–70% of US/UK rates — with English-fluent communication and IST working hours.

  • Hire TensorFlow developers in USA

    4–6 hours of daily overlap — your morning, our evening

    Hire dedicated TensorFlow developers for your USA team without the local price tag. US companies work with our vetted, full-time TensorFlow engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.

  • Hire TensorFlow developers in UK

    Overlap across your whole morning (our afternoon)

    Hire dedicated TensorFlow developers for your UK team without the local price tag. UK companies work with our vetted, full-time TensorFlow engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.

  • Hire TensorFlow developers in Canada

    4–6 hours of daily overlap with ET and PT

    Hire dedicated TensorFlow developers for your Canada team without the local price tag. Canada companies work with our vetted, full-time TensorFlow engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.

  • Hire TensorFlow developers in Australia

    Strong overlap — our morning is your afternoon

    Hire dedicated TensorFlow developers for your Australia team without the local price tag. Australia companies work with our vetted, full-time TensorFlow engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.

  • Hire TensorFlow developers in UAE

    Near-complete overlap — just 90 minutes apart

    Hire dedicated TensorFlow developers for your UAE team without the local price tag. UAE companies work with our vetted, full-time TensorFlow engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.

  • Hire TensorFlow developers in Germany

    Overlap through your morning and our afternoon (DACH)

    Hire dedicated TensorFlow developers for your Germany team without the local price tag. Germany companies work with our vetted, full-time TensorFlow engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.

  • Hire TensorFlow developers in Singapore

    Near-complete overlap — only 2.5 hours apart

    Hire dedicated TensorFlow developers for your Singapore team without the local price tag. Singapore companies work with our vetted, full-time TensorFlow engineers at 50–70% lower cost than local contractors, with enough daily overlap for live standups, pairing and code review.

TensorFlow developer rates

Hire dedicated TensorFlow developers from Krapton's team in India. Rates below are blended hourly bands; dedicated developers are billed monthly with no lock-in.

Get a tailored quote

  • Junior (1–3 yrs)

    Feature work under senior review

    $30–$40/hr

  • Mid-level (3–6 yrs)

    Owns modules end-to-end

    $40–$50/hr

  • Senior / lead (6+ yrs)

    Architecture, reviews, mentoring

    $50–$60/hr

Why teams hire TensorFlow developers from Krapton

Projects delivered
50+
In-house engineers
60+
Average client rating
4.8/5
On-time delivery
98%

  • Vetted, full-time engineers

    Every TensorFlow developer is a full-time, in-house Krapton engineer — never a freelancer or subcontractor.

  • Ready in 24 hours

    Shortlisted TensorFlow CVs within a business day and an onboarded developer within two.

  • Clear hourly rates

    Published hourly bands by seniority, billed monthly, with no lock-in.

  • Your IP, your repos

    Full IP assignment, free NDA on request, and all code in your accounts from day one.

  • 30-day replacement

    Not the right fit? Free replacement within five business days in the first 30 days.

  • Real timezone overlap

    4–6 hours of daily overlap with US, UK, EU, UAE and AU teams for live collaboration.

Services that pair well with TensorFlow

Most TensorFlow engagements also benefit from these Krapton services. Browse full offerings on the services page.

Hire TensorFlow developer

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Hiring TensorFlow developers — answered

Practical answers to the questions CTOs and founders ask us most often before they hire.

How quickly can I hire a TensorFlow developer from Krapton?

Within 24 hours of the initial call. We keep a bench of pre-vetted TensorFlow engineers, so once you confirm the brief we line up two or three shortlisted CVs within a business day and can onboard the selected developer within two.

How much does it cost to hire a dedicated TensorFlow developer from India?

Blended hourly rates for senior TensorFlow engineers typically fall in the $30–$60 USD range depending on seniority and engagement length. Dedicated developers are billed monthly with no lock-in.

Are your TensorFlow developers full-time employees or freelancers?

Every TensorFlow developer you hire through Krapton is a full-time, in-house engineer — we never subcontract to freelancers or agencies. That means consistent code quality, signed NDAs, and a single point of accountability for your project.

Who owns the code and IP my TensorFlow developers write?

You do — 100%. IP assignment and confidentiality are covered by the contract and a free NDA on request. All code lives in your repositories, on your infrastructure, under your accounts from day one.

What skills should I check before I hire TensorFlow developers?

Check production skills as well as modeling skills. A strong TensorFlow developer can build efficient tf.data pipelines, explain tf.function tracing and retracing, export a SavedModel with clean serving signatures, and convert and quantize a model for LiteRT. They should also know Keras 3 and how it differs from tf.keras in older code, and be able to read a TensorBoard profile to find whether training is input-bound or compute-bound.

Should a new project use TensorFlow or PyTorch?

For most new projects, especially anything involving large language models or recent research code, PyTorch is the safer default because that is where new models and libraries appear first. TensorFlow still makes sense if your team already runs it in production, if you deploy to TPUs, or if mobile, microcontroller or in-browser inference through LiteRT and TensorFlow.js is central to the product.

Can you migrate our models from TensorFlow to PyTorch, or the reverse?

Yes, but treat it as a re-implementation with verification, not a file conversion. Weights can be ported layer by layer, or exported through ONNX for inference-only use, but preprocessing, padding behavior and channel ordering often differ. Developers rebuild the architecture, load weights, and compare outputs on a fixed batch within a tolerance. Keras 3 can reduce the work, since the same model code can run on a PyTorch backend.

How do you make TensorFlow inference faster in production?

Start by measuring where time goes, then apply the simplest fix first. Common wins are server-side request batching in TF Serving, removing Python from the request path, using XLA compilation or exporting to TensorRT on NVIDIA GPUs, and quantizing or distilling the model when a small accuracy trade is acceptable. For mobile, LiteRT GPU delegates and int8 models usually matter most. Every change is benchmarked against the same latency and accuracy targets.

How do TensorFlow and Keras version upgrades affect existing models?

Most upgrades are smooth, but Keras 3 is the one that needs planning. Since TensorFlow 2.16, tf.keras points to Keras 3, which changed saving formats, some layer behavior and custom-object handling; older code can pin the tf-keras package while it is migrated. Developers also check CUDA and cuDNN compatibility for each release, note that native Windows GPU support ended after TensorFlow 2.10, and re-run evaluation after every upgrade.

Keep exploring

More ways to plan your TensorFlow build and team with Krapton.

Ready to build with TensorFlow?

Get a free 30-minute consultation with our TensorFlow team. Clear roadmap, transparent pricing, no obligation.

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  • Response within 24 hours
  • Certified TensorFlow developers
  • Flexible engagement models
  • US, UK, UAE & India clients served

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