Skill specialists
Hire Vetted TensorFlow Engineers
Find ML engineers with deep TensorFlow expertise for production model training, Keras integration, TFX pipelines, and mobile deployment via TFLite.
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Khandu
Akmal
Yuwetha
Daniel
Yash
Sandeep
Yash
Jesper
Olisaemeka
Marshaniswah
Hiring guide
Why companies hire TensorFlow experts
TensorFlow remains widely used in enterprise ML systems, particularly for production deployments, mobile (TFLite), and end-to-end ML pipelines using TFX. TensorFlow engineers bring structured deployment workflows and mature tooling to AI projects.
Popular projects
- Deploy a trained ML model to mobile or edge devices via TFLite
- Build end-to-end training and serving pipelines with TFX
- Migrate a research model to production-grade TensorFlow
- Serve models at scale with TensorFlow Serving or Vertex AI
What to look for
- Experience with Keras functional and subclassing API
- Knowledge of TFX components and pipeline orchestration
- Familiarity with TF2 eager execution and graph tracing
Frequently asked questions
Should I hire a TensorFlow or PyTorch engineer?
TensorFlow is stronger for structured enterprise pipelines, mobile/edge deployment via TFLite, and TFX workflows. PyTorch is preferred for research and LLM development. Many engineers on RemoteAI are fluent in both.
Is TensorFlow still relevant in 2025?
Yes — TensorFlow and Keras are still used heavily in production enterprise systems, healthcare AI, and edge applications. Many companies standardise on TF for its mature deployment tooling.
What is the difference between TensorFlow and Keras?
Keras is the high-level API that runs on top of TensorFlow (and now also PyTorch and JAX via Keras 3). TensorFlow handles the underlying computation. Most engineers use Keras for model design and TensorFlow for deployment and serving infrastructure.
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