Work Flexibility: Hybrid
Position Overview
We are seeking a visionary Technical Manager with 15+ years of overall experience to lead our AI engineering team. You must bring 5 years of dedicated experience managing, mentoring, and scaling technical teams. In this role, you will drive the development, training, and deployment of cutting-edge AI models, with a specific focus on medical imaging applications. This role balances technical architecture, hands-on modeling strategy, and people leadership.
What you will do:
Lead the end-to-end strategy for AI model development and production deployment.
Evaluate and implement emerging AI frameworks, tools, and cloud methodologies.
Oversee the training and optimization of deep learning models for imaging computer vision.
Guide the team in handling 2D/3D medical modalities like MRI, CT, X-ray, and Ultrasound.
Deep knowledge of deep learning for segmentation, detection, classification, registration, reconstruction, and longitudinal change analysis
CNNs, Transformers, U-Net variants, nnU-Net, and foundation/self-supervised models for imaging.
Architect robust MLOps pipelines for continuous model monitoring, testing, and deployment.
Optimize models for cloud, edge, and clinical environment hardware constraints.
What you need:
Required Qualifications:-
Master’s or Ph.D. in Computer Science, Biomedical Engineering, or a related field.
15+ years of experience in software engineering and data science.
5 years of direct experience managing and leading technical AI/ML teams.
Strong research background with demonstrated contributions in AI/ML through publications, patents, applied research, industrial innovation, or equivalent scientific work.
Deep knowledge of Machine Learning, Deep Learning, Natural Language Processing, Generative AI, Large Language Models, Agentic AI / AI Agents
Proven experience developing advanced AI models from research through implementation and evaluation.
Technical Skills
Core AI: Deep understanding of CNNs, Transformers, segmentation, and object detection.
Imaging Libraries: Expertise in DICOM, NIfTI, ITK, Monai, OpenCV, and PyTorch/TensorFlow.
MLOps: Hands-on experience with Docker, Kubernetes, Triton, AWS, GCP, or Azure ML.
Data Handling: Experience with medical data de-identification, curation, and active learning.
Preferred
Strong communication skills to bridge the gap between technical teams and medical experts.
Proven track record working with medical imaging data and clinical workflows.
Knowledge of clinical workflow integration: PACS/RIS/VNA, DICOM networking, study routing, and integration with hospital IT systems
Designed scalable infrastructure for processing high-resolution medical imaging datasets.
Ensured compliance with medical software standards, data privacy, and security protocols.