Software Engineer

We are looking for a Software Engineer with 2+ years of experience and a strong foundation in Machine Learning and Computer Vision to join a health tech project building real time AI assistance for clinicians performing ultrasound guided endoscopic procedures.

The system analyzes live ultrasound video and highlights anatomy, suspected pathology, and medical instruments frame by frame, helping clinicians understand where the AI model is focusing during a procedure.

This is a hands on engineering role where you will work across the segmentation model lifecycle, from curated clinical data and model training to evaluation, optimization, deployment, retraining, and direct collaboration with the client.

Benefits include Provident Fund, Medical Inpatient and Outpatient Facilities, Paid Overtime, Subsidized Lunch and Dinner, Gym Facility, Sports Allowance, Interest Free Loan Facility, Advance Salary options, and employee activities.

If you have hands on experience with Computer Vision and PyTorch, communicate confidently with clients, and are interested in applying AI to real time clinical workflows, we would love to hear from you.

Apply or reach out with your resume and a brief summary of your relevant Computer Vision, Medical Imaging, PyTorch, and client facing experience.

Requirements

Core Responsibilities

What you’ll work on:

• Train and improve multi label medical image segmentation models using PyTorch
• Work with ultrasound video datasets and clinician annotated medical imaging data
• Run experiments around class imbalance, overlapping anatomy, small or thin targets, and temporal context
• Build and improve evaluation pipelines with per label, frame level, and event level metrics
• Validate models using patient level test splits and clinician review
• Optimize inference for real time video processing with a focus on GPU latency and performance
• Work with mixed precision, model compilation, batching, frame stride, and other inference optimization techniques
• Package and deploy models using Docker and AWS based infrastructure
• Contribute to reproducible training and retraining pipelines with experiment tracking, checkpointing, CI, and automated testing
• Collaborate with clinicians, annotators, infrastructure engineers, and external ML partners
• Participate in regular client calls, technical discussions, demos, requirement discussions, and progress updates
• Explain technical decisions, experiment results, risks, and tradeoffs clearly to both technical and non technical stakeholders
• Work directly with the client to understand feedback, clarify requirements, and translate discussions into actionable engineering tasks