Keras is the high-level deep learning API that powers more production neural networks than almost any other interface — running on TensorFlow, PyTorch, or JAX backends. Its appeal is rapid iteration: a model that would take 200 lines in raw TensorFlow often takes 20 in Keras. But rapid prototyping is only valuable if it leads to robust production code. Post your Keras job on RemoteAI and hire a developer who knows how to take a Keras model from notebook to deployment.
Talent pool
These are the types of Keras developers available on RemoteAI. New profiles are reviewed by our team.
Image classification, regression, NLP, time series, or multi-modal — and whether the model will run on a server, mobile device, or embedded system. The target determines which Keras backend and export format matters.
Look for developers who have built and deployed your specific type of network — not just familiarity with the API but practitioners who understand regularization, overfitting strategies, and production export for your deployment context.
Assess how candidates handle the gap between training accuracy and production performance — regularization choices, model export format, and serving strategy are the real signal.
What you negotiate is what you pay. RemoteAI charges nothing on contracts.
Hiring a keras developer remotely starts with a clear, well-scoped job post. This guide covers what to include, how RemoteAI's matching works, and what you can expect at each step of the process.
Keras developers on RemoteAI are typically neural network generalists who can move quickly from problem to working model. The core skills in demand: designing model architectures using the Keras Functional or Sequential API, implementing custom layers and loss functions, applying transfer learning with pre-trained Keras Application models (ResNet, EfficientNet, BERT), training with callbacks (early stopping, learning rate scheduling), and converting trained models to TFLite, ONNX, or TF Serving formats for deployment. Since Keras 3.0 runs natively on TensorFlow, PyTorch, and JAX, senior Keras developers also understand the backend they are targeting — choosing the right one for training speed, memory efficiency, or deployment compatibility.
A well-structured job post attracts the right keras developers and filters out irrelevant applications. Here is the exact process to follow on RemoteAI.
Image classification, regression, NLP, time series, or multi-modal — and whether the model will run on a server, mobile device, or embedded system. The target determines which Keras backend and export format matters.
Look for developers who have built and deployed your specific type of network — not just familiarity with the API but practitioners who understand regularization, overfitting strategies, and production export for your deployment context.
Assess how candidates handle the gap between training accuracy and production performance — regularization choices, model export format, and serving strategy are the real signal.
What you negotiate is what you pay. RemoteAI charges nothing on contracts.
Looking to browse talent directly?
View Keras Developer Jobs and contact them directly, no job post needed.
Specify the neural network task type (image, text, tabular, multi-modal), the Keras backend (TensorFlow, PyTorch, or JAX), whether this involves training from scratch or fine-tuning a pre-trained model, and the deployment target — TFLite, ONNX, or TF Serving.
Keras is the high-level API for building and training neural networks; TensorFlow is one of its underlying backends. Most TF2 code is written in Keras. If your project involves building and training neural networks, you need Keras experience — but for production deployment infrastructure like TF Serving or TFX, you also need TensorFlow ecosystem knowledge.
Free for one job a month (open 14 days; jobs from companies without a verified website are reviewed first, paid plans for more). No agency commission. You pay your Keras developer the rate you negotiate directly.
Developers who specialize in production architecture design, custom training loops and inference optimization usually charge more. Rates depend on seniority, country and scope. Put a budget or rate range in your job post and ask candidates for their rate in the first message; RemoteAI takes no placement fee on what you pay them.
Create a free account and describe the neural network task, the Keras backend, and the deployment target. RemoteAI's matching ranks applicants by the skills and level in your post.