Role Overview,841
Seeking an experienced Deep Learning Engineer to lead the design, tuning, and evaluation of advanced ML/DL architectures for high-performance vision algorithms. The role involves solving complex modeling challenges, optimizing datasets, and collaborating on deployment strategies for real-world production environments.
Core Responsibilities
- Design, refine, and evaluate deep learning models (e.g., U-Net, YOLO variants).
- Explore and experiment with new modeling approaches for challenging vision tasks.
- Lead data purification, augmentation, and dataset optimization with transfer-learning considerations.
- Define performance metrics and analyze model results across multiple tasks.
- Present findings through visualizations, documentation, and technical reviews.
- Collaborate with engineering teams to align algorithmic solutions with deployment constraints.
Required Qualifications
- PhD with 5+ years of relevant experience OR MS/MTech with 8+ years in algorithm development.
- Strong background in deep learning and computer vision (minimum 5 years).
- Hands-on experience with PyTorch and end-to-end model training workflows (minimum 5 years).
- Strong communication skills with the ability to lead technical discussions.
- Stable career history (no frequent job changes or employment gaps).
Preferred Qualifications
- Experience with experiment-tracking tools (TensorBoard, Weights & Biases).
- Familiarity with cross-platform development, integration, and system-level workflows.
Mandatory Notes
- Candidates must come from product-based, semiconductor, engineering product, or hardware manufacturing companies.
- Only candidates with full-time engineering degrees (No B.Sc/BCA backgrounds).
Interview Process
- 3 Technical Rounds
- HR Discussion
Work Model
Skills: computer vision,modeling,algorithm development,learning,deep learning,pytorch
