Hire TensorFlow developers who train neural networks that hold up past the first epoch chart.
Deep learning built specifically on TensorFlow, custom architectures, distributed training at scale, and production deployment through TF Serving, by engineers who validate a network on real held-out data, not a loss curve that looks good and nothing else. Distinct from our scikit-learn role, which covers classical, non-neural ML for tabular data.
Custom neural network architectures matched to the actual problem, not a default architecture copied from a tutorial.
Distributed training across GPUs/TPUs, with data pipelines that don't bottleneck the hardware.
Held-out validation and ablation testing that separates a real improvement from noise.
TF Serving or TFLite deployment tuned for your actual latency and device constraints.
What strong TensorFlow Developers know cold.
from $25/hr
A typical range. Your final rate depends on experience level, timezone overlap and how long you book for.
Four stages. Three percent get through.
- 01Screening38%Résumé, background, and communication check, the fastest way to rule out a bad fit.
- 02Technical Deep Dive22%A senior engineer probes real system design and stack depth, not trivia.
- 03Live Build Test9%A timed, real-world task. We watch how they actually ship, not just what they claim.
- 04Client Fit Interview3%Ownership, reliability, and how they work inside your team on day one.
- Shortlist in 48 hoursProfiles, not a waiting list.
- Free replacementWrong fit swapped at no cost.
- No conversion feeHire them direct whenever you want.
- IP yours from day oneAssigned in writing, not at handover.
- Month to monthNo lock-in, and no notice period.
Shipped, not slideware.
On camera, in their own words.
Why he brought his development work to Code Elevator.
Hiring for something adjacent?
Answered before you ask.
Both are viable production frameworks; TensorFlow's edge is its deployment tooling (TF Serving, TFLite, TensorFlow.js), and its existing ecosystem in mobile and edge inference. We'll recommend based on your actual deployment target, not a default preference.
Distributed training across multiple GPUs or TPUs is core to this role, with data pipelines profiled so added hardware actually translates into faster training.
Yes. Via TFLite, optimized and quantized for the target device's real constraints, not just exported and hoped to fit.
Held-out validation and ablation testing, not just the training loss. We report accuracy on data the model never saw, and we'll say plainly where it's still weak.
Every way out of this hire is already written down.
Not a fit? Replace anyone in the first two weeks. No questions asked, no replacement fee. We re-match from the same vetted pool.
You pay only for engineers who actually start. Seeing candidates costs nothing.
Dedicated and managed engagements run on a monthly rolling contract. Cancel with notice.
You see the number before you commit. Nothing is added on top of it later.
Bring us the training problem. Three matched engineers in 48 hours.
Whatever the network needs to learn. We'll scope the architecture and validation plan before the first training run starts.
We reply within an hour during our working day in India and the UAE.