Junior AI Engineer

We are building AI-powered healthcare products focused on medical imaging, clinical workflows, and decision-support systems for hospitals and diagnostic networks.

We are looking for a Junior AI Engineer / AI Engineer who is eager to work on real-world AI problems in healthcare. The role is suitable for a fresh graduate or early-career engineer with strong fundamentals in Python, machine learning, deep learning, and computer vision.

Prior experience in medical imaging is helpful but not mandatory. What matters most is the ability to learn quickly, work with real data, and build reliable AI systems under guidance.

• Assist in developing and improving AI models for healthcare use cases such as:
• · Medical image classification
• · Disease detection and triage
• · Image segmentation and localization
• · Report/text-based clinical AI features
• · Multimodal AI using images, reports, and structured data
• Work with senior team members to train, test, fine-tune, and evaluate models using frameworks such as PyTorch, TensorFlow, MONAI, or similar tools.

• Work on image preprocessing and computer vision tasks, including:
• · Cleaning and preparing image datasets
• · Image augmentation and normalization
• · Object detection, classification, and segmentation
• · Working with X-rays, CT scans, MRI, ultrasound, or other imaging formats
• · Learning and handling DICOM-based medical images

• Support healthcare data pipelines, including:
• · Organizing datasets
• · Checking data quality
• · Preparing training, validation, and test sets
• · Assisting with annotation workflows
• · Coordinating with clinical experts for label review
• · Helping identify data gaps, bias, and edge cases

• Help evaluate model performance using metrics such as:
• · Accuracy
• · Precision and recall
• · Sensitivity and specificity
• · AUC
• · F1 score
• · False positive and false negative analysis
• Learn how model performance translates into clinical usefulness and safety.

• Assist in moving AI models from experiments to usable products. This may include:
• · Writing inference scripts
• · Creating simple APIs
• · Using Docker for deployment
• · Supporting cloud or on-premise model deployment
• · Monitoring model outputs
• · Maintaining model and dataset versions

• · Strong Python programming skills
• · Basic understanding of machine learning and deep learning
• · Familiarity with PyTorch, TensorFlow, Keras, or similar frameworks
• · Basic computer vision knowledge
• · Familiarity with OpenCV, NumPy, pandas, scikit-learn, or similar libraries
• · Ability to read research papers, documentation, and implement ideas
• · Good debugging and problem-solving skills
• · Willingness to work with messy real-world healthcare data
• · Strong learning mindset

• These are good to have, but not mandatory:
• · Internship, academic project, or personal project in AI / ML / computer vision
• · Experience with medical imaging or healthcare datasets
• · Familiarity with DICOM, pydicom, MONAI, SimpleITK, 3D Slicer, or nnU-Net
• · Experience with CNNs, Vision Transformers, YOLO, U-Net, or segmentation models
• · Basic knowledge of APIs, Docker, Git, or cloud platforms
• · Exposure to LLMs or multimodal AI
• · Understanding of clinical validation, healthcare workflows, or hospital software systems

• Fresh graduates or candidates with 0-2 years of experience are welcome. Preferred backgrounds include:
• · Computer Science
• · Artificial Intelligence / Machine Learning
• · Data Science
• · Biomedical Engineering
• · Electronics / Electrical Engineering
• · Mathematics / Statistics
• · Related technical fields
• Strong projects, internships, GitHub work, Kaggle work, or practical AI experience will be valued.

• The ideal candidate is someone who:
• · Has strong fundamentals and is eager to learn
• · Has built at least a few AI / ML / computer vision projects
• · Can work independently after guidance
• · Is comfortable experimenting, failing, debugging, and improving models
• · Is interested in healthcare AI and medical imaging
• · Wants to build products used in real clinical environments
• · Is practical, hardworking, and execution-focused

• You will get exposure to:
• · Medical imaging AI
• · DICOM and hospital imaging workflows
• · Real-world clinical datasets
• · Model training and validation
• · AI deployment in healthcare settings
• · Working with doctors, hospitals, and product teams
• · Building AI systems beyond academic demos