Machine Learning Engineer

Location: Fully Remote (India-based) Working Hours: 3:00 PM – 12:00 AM IST Type: Full-time

This is a full-time role with ProfoundIQ, 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

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• 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.

• Train, tune, update, and deploy deep learning models into the client's production environment
• Maintain low-latency, on-premise inference pipelines using PyTorch, ONNX, TensorRT, 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
  

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• 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 DevOps boundaries
  

• Experience working with real-time video streams
• Experience with GStreamer, FFmpeg, or RTSP (or similar) video pipelines
• Experience with Triton Server, model optimization using TensorRT, and other deep learning acceleration frameworks
• Experience configuring and deploying hardware video multiplexers