Software Engineer, AI/ML

We are seeking a Software Engineer with over two years of experience and a solid background in Machine Learning and Computer Vision to contribute to an innovative health tech project. This role involves developing real-time AI assistance tools for clinicians performing ultrasound-guided endoscopic procedures. The system processes live ultrasound video, identifying anatomical structures, suspected pathologies, and medical instruments frame by frame, providing clinicians with insights into the AI model’s focus during procedures.

This is a hands-on engineering position where you will engage in the full lifecycle of segmentation models—from working with curated clinical datasets and model training to evaluation, optimization, deployment, and retraining. You will collaborate closely with clinicians and clients to ensure the AI solutions meet clinical needs effectively.

Key Responsibilities

- Train and enhance multi-label medical image segmentation models using PyTorch.
- Handle ultrasound video datasets and clinician-annotated medical imaging data.
- Conduct experiments addressing challenges such as class imbalance, overlapping anatomy, small or thin targets, and temporal context.
- Develop and refine evaluation pipelines with metrics at per-label, frame, and event levels.
- Validate models using patient-level test splits and clinician feedback.
- Optimize inference for real-time video processing, focusing on GPU latency and overall performance.
- Utilize techniques such as mixed precision training, model compilation, batching, and frame stride for inference optimization.
- Package and deploy models using Docker and AWS infrastructure.
- Contribute to reproducible training and retraining pipelines incorporating experiment tracking, checkpointing, continuous integration, and automated testing.
- Collaborate with clinicians, annotators, infrastructure engineers, and external machine learning partners.
- Participate in regular client meetings, technical discussions, demos, requirement gathering, and progress updates.
- Clearly communicate technical decisions, experiment outcomes, risks, and trade-offs to both technical and non-technical stakeholders.
- Work directly with clients to understand feedback, clarify requirements, and translate discussions into actionable engineering tasks.

Required Qualifications

- Bachelor’s degree in Computer Science, Artificial Intelligence, or a related field.
- Minimum of 2 years’ experience in software engineering, machine learning, or computer vision.
- Strong proficiency in Python programming.
- Hands-on experience with PyTorch and training image segmentation or computer vision models.
- Solid understanding of model training, validation, evaluation, and inference workflows.
- Familiarity with data loaders, mixed precision training, checkpointing, and GPU-based training.
- Experience working with video or image datasets and best practices for train-test data separation.
- Knowledge of AWS, cloud GPU environments, object storage, and Docker.
- Understanding of production ML inference and performance optimization.
- Strong software engineering fundamentals including testing, version control, reproducible configurations, and continuous integration.
- Excellent verbal and written communication skills.
- Confidence in participating in client meetings and technical discussions.
- Ability to clearly explain technical topics, ask relevant questions, and proactively communicate blockers or risks.
- Capability to discuss experiments, model results, implementation decisions, and trade-offs directly with clients.

Preferred Qualifications and Benefits

Preferred qualifications include experience with medical imaging or medical image segmentation, familiarity with medical video annotation platforms, and real-time computer vision or video inference expertise. Knowledge of multiple segmentation architectures or frameworks, multi-GPU training, model deployment, and inference optimization is advantageous. Prior experience working directly with clients, product teams, or external stakeholders is highly valued. Awareness of Software as a Medical Device (SaMD) practices, including model traceability, model cards, audit trails, and clinical acceptance processes, is a plus.

Benefits offered include Provident Fund, comprehensive medical inpatient and outpatient facilities, paid overtime, subsidized lunch and dinner, gym access, sports allowance, interest-free loan facilities, advance salary options, and various employee engagement activities.