Machine Learning Engineer

Location:

Fully Remote (India-based)

Working Hours:

3:00 PM – 12:00 AM IST  Type:  Full-time

About the Role This is a full-time role with  Profound

IQ , working as part of a team deployed to one of our clients.
The client builds vision agents for large venues such as hotels and casinos, powering real-time video analytics and intelligent surveillance across hundreds of camera streams. Their systems run on-premise in some of the largest resorts in Las Vegas, with many more in the pipeline.
You'll join a highly technical team shipping deep tech into one of the most operationally demanding and dynamic environments out there.
We're looking for a Machine Learning Engineer who blends strong technical ML/CV ability with comfort supporting and deploying systems in live production environments. You will own real-time vision pipelines end-to-end and act as the technical face of the engineering effort for the client.

This is not a back-office research job.  You will:
Ship models into production
Debug live production pipelines in the client's environment
Build new ML features spanning classical ML, computer vision, and LLMs
Work hands-on with GPU servers and multi-camera systems
Collaborate with the client's surveillance teams and distribution partners
If you love solving real-world problems in messy environments, this is your role.

What You'll Do  Train, tune, update, and deploy deep learning models into the client's production environment
Maintain low-latency, on-premise inference pipelines using PyTorch, ONNX, Tensor

RT, and Triton
'Build training-data processing pipelines, handle QA/QC of labeling, and coordinate work with the labeling teams
Work closely with customers and the product manager to experiment and ship new features

What We're Looking For   4–6 years of machine learning experience , with strong knowledge of both deep learning  and  classical ML. You're an ML engineer first — someone who can train models, tune them, debug them in the wild, and build the software around them to make them production-ready.
Hands-on experience with computer vision  — building, training, and deploying CV models in production
Strong skills in  Linux, Docker, and shipping models as services
Comfortable working in live production environments with minimal supervision
A startup mindset: resourceful, adaptable, and excited to work across ML, backend, and Dev Ops boundaries

Nice to Have  Experience working with  real-time video streams

Experience with  GStreamer, FFmpeg, or RTSP  (or similar) video pipelines

Experience with  Triton Server , model optimization using  Tensor

RT , and other deep learning acceleration frameworks
Experience configuring and deploying hardware video multiplexers