Software Engineer, AI/ML

Devsinc, LLC; Islamabad; Presencial; PKR 1.800.000 - 3.600.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Provident Fund

Medical inpatient/outpatient

Paid overtime

Subsidized meals

Gym facility

Sports allowance

Interest-free loan

Advance salary options

Employee activities

Descripción de la vacante

Devsinc, LLC is seeking a Software Engineer with 2+ years of experience in ML and Computer Vision to advance a health tech project enabling real-time AI assistance during ultrasound-guided procedures.

You will handle the segmentation model lifecycle, from data preparation and training to evaluation, deployment, and retraining, while collaborating closely with clinicians and the client.

Formación

• Minimum: Bachelor's in Computer Science / Artificial Intelligence.
• 2+ years of relevant software engineering, Machine Learning, or Computer Vision experience.
• Hands-on experience with PyTorch.
• Experience training image segmentation or Computer Vision models.
• Understanding of training, validation, evaluation, and inference workflows.
• Familiarity with data loaders, mixed precision training, checkpointing, and GPU-based training.
• Experience with AWS, cloud GPU environments, Docker, and production ML inference.
• Strong software engineering fundamentals including testing, version control, reproducible configurations, and CI.
• Strong verbal and written communication skills.

Responsabilidades

• Train and improve multi-label medical image segmentation models using PyTorch.
• Work with ultrasound video datasets and clinician annotated data.
• Run experiments around class imbalance, overlapping anatomy, and small targets.
• Build 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 GPU latency focus.
• Package and deploy models using Docker and AWS infrastructure.
• Collaborate with clinicians, annotators, and engineers; participate in client calls and demos.
• Explain technical decisions and risks clearly to stakeholders.

Conocimientos

Python

PyTorch

Machine Learning

Computer Vision

Model Evaluation

CI/CD

Version Control

Client Communication

Educación

Bachelor's in Computer Science/Artificial Intelligence

Herramientas

Docker

AWS

Git

CI Tools

Descripción del empleo

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.

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

Qualification

Minimum: Bachelors in Computer Science/Artificial Intelligence

Required Skills

What we’re looking for:

• 2+ years of relevant software engineering, Machine Learning, or Computer Vision experience
• Strong Python programming skills
• Hands on experience with PyTorch
• Experience training image segmentation or Computer Vision models
• Understanding of model training, validation, evaluation, and inference workflows
• Familiarity with data loaders, mixed precision training, checkpointing, and GPU based training
• Understanding of video or image based datasets and good practices around train and test data separation
• Experience or familiarity with 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 CI
• Strong verbal and written communication skills
• Confidence participating in client meetings and technical discussions
• Ability to explain technical topics clearly, ask the right questions, and communicate blockers or risks proactively
• Ability to discuss experiments, model results, implementation decisions, and tradeoffs directly with the client

Nice to have:

• Experience with medical imaging or medical image segmentation
• Experience with medical video annotation platforms
• Real time Computer Vision or video inference experience
• Familiarity with multiple segmentation architectures or frameworks
• Experience working with multi GPU training
• Knowledge of model deployment and inference optimization
• Previous experience working directly with clients, product teams, or external stakeholders
• Awareness of Software as a Medical Device practices, model traceability, model cards, audit trails, or clinical acceptance processes