Skip to content
Hire us

Hire expert PyTorch developers

Add a PyTorch engineer who can take a model from a research notebook to a profiled, reproducible training run and a served artifact that meets your latency and memory budget.

Hire a developerView projects
Projects delivered
50+
Average rating
4.8/5
Response time
24h

Hire a PyTorch developer

Tell us what you're building. We'll send a plan and a quote within 24 hours — no spam.

We respond within one business day. No spam.

or

Prefer to talk first? Schedule a free call

A cleaner way to hire PyTorch developers

Hire PyTorch developers from Krapton when you need engineers who can design, train, debug and ship deep learning models rather than only call a hosted API. Our PyTorch developers work across the whole loop: writing nn.Module architectures...

Shortlist
24h
Replacement
30d
IP yours
100%

  • Research code into production

    Most model code starts as a notebook with hard-coded paths and global state. A PyTorch developer restructures it into configurable modules, deterministic data pipelines and checkpointed training scripts, so a run can be resumed after a spot-instance preemption and reproduced months later by someone else.

  • GPU budgets get respected

    GPU hours are usually the largest line in an ML budget. Engineers who know bf16 autocast, activation checkpointing, efficient DataLoader workers and the PyTorch profiler can often fit a larger batch on the same card or finish a run without renting a bigger instance.

  • Numerical bugs found faster

    Silent failures are common in deep learning: a loss that plateaus, NaNs after a few thousand steps, a model that trains but never generalizes. Our developers inspect gradient norms, use autograd anomaly detection and overfit a single batch first to separate data bugs from modeling bugs.

  • Own models, not rented ones

    When a hosted API is too expensive per call, too slow, or not allowed to see your data, a fine-tuned open model served on your own infrastructure becomes the alternative. PyTorch is the layer that makes that adaptation, evaluation and deployment possible.

Why PyTorch?

What makes PyTorch the right choice for modern engineering teams.

  • Dynamic computation graphs

    Build and modify neural networks on-the-fly with autograd for flexible research.

  • GPU acceleration

    Seamless CUDA integration for multi-GPU training and tensor operations.

  • TorchScript

    Export models to a production-ready, language-independent representation.

  • torch.compile

    Just-in-time compilation for 2-4x inference speedup without model changes.

  • Distributed training

    torch.distributed and FSDP for training billion-parameter models across clusters.

  • HuggingFace integration

    Native compatibility with the Transformers, Datasets, and Accelerate libraries.

What you can build with PyTorch

  • Custom vision models

    Detection, segmentation and classification models built on torchvision backbones or timm, fine-tuned on your labeled images, with augmentation pipelines, class-imbalance handling and evaluation on a held-out set that reflects real camera conditions.

  • Fine-tuned language models

    Open-weight LLMs adapted to your domain with LoRA or QLoRA through PEFT, trained on curated instruction data, and evaluated against a fixed test set before the adapter is merged or served alongside the base model.

  • Speech and audio pipelines

    Transcription, speaker diarization and audio classification built on open speech models, with feature extraction, voice activity detection, streaming chunked inference and the latency work needed for near-real-time use.

  • Recommendation and ranking models

    Two-tower retrieval and ranking networks that learn embeddings for users and items, trained on interaction logs with negative sampling, then exported so candidate retrieval runs against a vector index at request time.

  • Time-series forecasting networks

    Sequence models for demand, sensor or financial signals when classical methods stop capturing the patterns, with proper walk-forward validation so the model is never evaluated on data from its own future.

  • Optimized inference services

    Trained checkpoints turned into production endpoints: torch.compile or ONNX Runtime for speed, int8 or 4-bit quantization for memory, dynamic batching on Triton Inference Server, and load tests that confirm the p95 latency you need.

Where PyTorch projects usually go wrong

Most stalled PyTorch projects do not fail because of the model architecture. They fail on data: labels that disagree with each other, a validation split that leaks near-duplicates from training, or preprocessing that differs between the training script and the serving code. Krapton’s PyTorch developers start by auditing the dataset and writing the evaluation harness before tuning anything, because a metric you cannot trust makes every later experiment meaningless. They overfit a tiny batch to confirm the model can learn at all, establish a simple baseline, and only then scale up model size or training time. The same transforms module is imported by both training and inference code, so a resize or normalization mismatch cannot creep in. Each experiment is logged with its config, data version and git commit, which means a good result can be reproduced instead of remembered.

The second common failure is the gap between a trained checkpoint and a usable service. A model that scores well offline can still be too slow, too large for the available GPU memory, or brittle on inputs that look different from the training set. Our engineers treat deployment as part of the modeling work: they set latency and memory budgets early, test torch.compile and quantization against the accuracy floor, choose between serving the model in Python and exporting through torch.export or ONNX, and add batching where traffic allows it. After launch they log input statistics and prediction distributions so drift shows up on a dashboard rather than in customer complaints. When the right answer is a smaller model, a hosted API or a classical method from scikit-learn, they say so, since the goal is a working system rather than a bigger network.

PyTorch in action

import torch
import torch.nn as nn

class TransformerBlock(nn.Module):
    def __init__(self, d_model=512, n_heads=8, dropout=0.1):
        super().__init__()
        self.attn = nn.MultiheadAttention(d_model, n_heads, batch_first=True)
        self.ff   = nn.Sequential(nn.Linear(d_model, d_model * 4), nn.GELU(), nn.Linear(d_model * 4, d_model))
        self.norm1, self.norm2 = nn.LayerNorm(d_model), nn.LayerNorm(d_model)
        self.drop = nn.Dropout(dropout)

    def forward(self, x, mask=None):
        attn_out, _ = self.attn(x, x, x, attn_mask=mask)
        x = self.norm1(x + self.drop(attn_out))
        return self.norm2(x + self.drop(self.ff(x)))

model = TransformerBlock().to('cuda')
optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4)

What our PyTorch developers know

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

Hire a developerSchedule a call
  • Custom model architectureBuilding encoder-decoder, transformer, and CNN architectures from scratch.
  • Training loopsWriting efficient training loops with mixed-precision, gradient accumulation, and LR schedulers.
  • Model debuggingUsing torch.autograd.detect_anomaly and gradient inspection for numerical issues.
  • ONNX exportExporting PyTorch models to ONNX for cross-framework deployment.
  • Fine-tuning LLMsParameter-efficient fine-tuning with LoRA and QLoRA using PEFT library.
  • Inference optimizationQuantization, pruning, and model compilation for production latency targets.

What our PyTorch developers go deep on

  • Training loop engineering

    Mixed precision with torch.autocast, gradient accumulation and clipping, learning-rate warmup and schedulers, checkpoint save and resume, and deterministic seeding so two runs with the same config produce comparable results.

  • Distributed and multi-GPU training

    DistributedDataParallel for data parallelism, FSDP for sharding parameters and optimizer state, torchrun launch scripts, and NCCL debugging when ranks hang or throughput drops on multi-node clusters.

  • LoRA and QLoRA fine-tuning

    Adapter training with PEFT on top of Transformers models, 4-bit base weights through bitsandbytes for QLoRA, rank and target-module selection, and merging adapters only after evaluation confirms the gain.

  • Export and compilation paths

    torch.compile for in-process speedups, torch.export and the ONNX exporter for portable graphs, with new deployment work built on torch.export because TorchScript is no longer the recommended path.

  • Profiling and memory debugging

    torch.profiler traces viewed in Perfetto or the Chrome trace viewer, CUDA memory snapshots to find fragmentation and leaks, and data-loader timing to prove whether the GPU is actually the bottleneck.

  • Experiment tracking and reproducibility

    Weights & Biases or MLflow for metrics and artifacts, versioned datasets, pinned CUDA and PyTorch versions in container images, and model cards recording exactly what data and config produced each checkpoint.

Common PyTorch projects we deliver

  • Moving a research prototype into production

    A data scientist has a promising notebook. The PyTorch developer separates config from code, adds a reproducible data split, writes unit tests for transforms and the model’s forward pass, and packages training so it runs the same way on a laptop and a GPU cluster.

  • Scaling training beyond a single GPU

    When a model or batch no longer fits on one card, the developer chooses between DistributedDataParallel, FSDP and activation checkpointing, launches with torchrun, and checks that throughput actually scales instead of stalling on data loading or network communication.

  • Cutting inference latency on existing models

    A model works but is too slow or too expensive to serve. The engineer profiles it, tries torch.compile, half precision and quantization, measures accuracy loss after each change, and reports which combination meets the latency target.

  • Replacing a paid API with open models

    For a narrow, high-volume task such as classification or extraction, a small fine-tuned model can replace general-purpose API calls. The developer builds the evaluation set first, then compares the fine-tuned model against the API on accuracy, latency and serving cost.

PyTorch solutions by industry

  • Healthcare imaging

    Segmentation and classification models for radiology or pathology images, with MONAI tooling, patient-level data splits to prevent leakage, and audit trails for every training dataset version.

  • Manufacturing

    Visual defect detection on production lines, often trained on few defect examples, exported to ONNX or TensorRT for edge devices that sit next to the camera.

  • Retail and e-commerce

    Product recommendation, visual search and catalog attribute extraction, where embeddings trained on your own click and purchase data usually reflect your catalog better than generic ones.

  • Financial services

    Fraud and anomaly detection on transaction sequences and document understanding for statements and forms, with model explainability and monitoring for drift as customer behavior changes.

PyTorch vs TensorFlow

Both frameworks can train and serve the same kinds of models, so the choice is mostly about ecosystem and existing investment. PyTorch dominates current research code and the Hugging Face model catalog. TensorFlow still has strong tooling for mobile and browser deployment through LiteRT and TensorFlow.js, and many teams keep long-running TensorFlow systems that are not worth rewriting.

PyTorch vs TensorFlow
DimensionPyTorchTensorFlow
Research adoptionDefault for most new papers and codeLess common in recent research releases
Programming modelEager by default, torch.compile for graph speedupsEager in TF2, tf.function for graph execution
Model hub supportMost new open checkpoints ship PyTorch weightsFewer new checkpoints offered in TensorFlow format
Mobile and browserExecuTorch for on-device, ONNX for broader runtimesMature LiteRT and TensorFlow.js deployment paths
Distributed trainingDDP, FSDP and torchrun built into coretf.distribute strategies, strong TPU integration
Production servingTriton, ONNX Runtime, vLLM or custom servicesTensorFlow Serving with SavedModel, long production history

How to hire PyTorch 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 PyTorch 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 PyTorch 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 PyTorch 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 PyTorch 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 PyTorch builds like an MVP, a migration or a specific module.

    • Scope locked upfront
    • Milestone acceptance
    • Predictable budget

Compare ways to hire PyTorch developers

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

Compare ways to hire PyTorch 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 PyTorch 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 PyTorch developers in India

    Full working-day overlap

    Krapton is an on-shore PyTorch studio in New Delhi. Hire PyTorch 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 PyTorch developers in USA

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

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

  • Hire PyTorch developers in UK

    Overlap across your whole morning (our afternoon)

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

  • Hire PyTorch developers in Canada

    4–6 hours of daily overlap with ET and PT

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

  • Hire PyTorch developers in Australia

    Strong overlap — our morning is your afternoon

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

  • Hire PyTorch developers in UAE

    Near-complete overlap — just 90 minutes apart

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

  • Hire PyTorch developers in Germany

    Overlap through your morning and our afternoon (DACH)

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

  • Hire PyTorch developers in Singapore

    Near-complete overlap — only 2.5 hours apart

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

PyTorch developer rates

Hire dedicated PyTorch 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 PyTorch 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 PyTorch developer is a full-time, in-house Krapton engineer — never a freelancer or subcontractor.

  • Ready in 24 hours

    Shortlisted PyTorch 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 PyTorch

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

Hire PyTorch developer

Free consultation · No commitment

Free NDA · No commitment · Response in 24 hours

Hiring PyTorch developers — answered

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

How quickly can I hire a PyTorch developer from Krapton?

Within 24 hours of the initial call. We keep a bench of pre-vetted PyTorch 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 PyTorch developer from India?

Blended hourly rates for senior PyTorch 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 PyTorch developers full-time employees or freelancers?

Every PyTorch 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 PyTorch 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.

When I hire PyTorch developers, what should a senior engineer already know?

A senior PyTorch engineer should be able to write a training loop from scratch without a framework hiding it, explain autograd and when tensors keep graph history, and debug shape, device and dtype errors quickly. Expect hands-on experience with mixed precision, DistributedDataParallel or FSDP, the profiler, and at least one export path such as torch.export or ONNX. They should also design evaluation sets that reflect production data, not just report training loss.

Should we use PyTorch or TensorFlow for a new project?

For most new projects, PyTorch is the safer default because current research code, open model checkpoints and the Hugging Face libraries target it first. TensorFlow remains reasonable if you already run TensorFlow Serving in production, depend on TensorFlow.js in the browser, or have a mature TFX pipeline. Rewriting a stable TensorFlow system purely to switch frameworks rarely pays off; adding PyTorch for new models alongside it often does.

How do you migrate a TensorFlow or Keras model to PyTorch?

Migration usually means re-implementing the architecture in PyTorch and porting the trained weights rather than relying on an automatic converter. If the model is pure Keras 3 code, switching its backend to PyTorch may be a shorter path. Otherwise the developer maps each layer, transposes weights where layout conventions differ, and compares outputs on identical inputs until they match within tolerance. Preprocessing is ported and tested separately, since mismatched normalization often causes post-migration accuracy loss.

How do you test and secure PyTorch models before deployment?

Testing covers code, data and behavior: unit tests check tensor shapes and that a forward and backward pass run, regression tests compare metrics against the last approved checkpoint, and slice evaluations look for groups where accuracy drops. For security, checkpoints are loaded with torch.load and weights_only=True or stored as safetensors, because pickle-based files can execute arbitrary code. Model files from public hubs are scanned and pinned to a specific revision.

How do you handle PyTorch version upgrades?

Upgrades are planned changes, with the PyTorch and CUDA builds pinned together in the container image so nothing shifts silently. The developer reads the release notes for deprecations, runs the test suite and a short training run, and compares loss curves and inference outputs against the previous version. Defaults do change between releases, as when torch.load switched to weights-only loading, so behavior checks matter as much as a passing build.

Keep exploring

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

Ready to build with PyTorch?

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

  • Free NDA on request
  • Response within 24 hours
  • Certified PyTorch developers
  • Flexible engagement models
  • US, UK, UAE & India clients served

Let's start your first project at reasonable prices.

Tell us what you want to build. We'll suggest the right PyTorch developer, timeline, and budget-friendly first milestone.

10% Discount

Highlighted starter offer for new project enquiries.

Hire a PyTorch developer

Share the basics. No commitment, just a useful reply.

Free consultation · NDA available · Response within 24 hours