Key Responsibilities

• Design & Implement Computer Vision Solutions: Develop and optimize end-to-end computer vision pipelines using advanced deep learning techniques.
• LLMs-VLMs: Have awareness about the latest technologies in the field of LLMs and VLMs.  
• Model Development & Deployment: Build, train, and deploy deep learning models (e.g., object detection, image segmentation, classification) using frameworks like PyTorch and TensorFlow.
• Data Pipeline & Infrastructure: Work closely with data engineers to ensure efficient data preprocessing, augmentation, and real-time inference pipelines.
• Docker & REST APIs: Containerize applications for scalable deployments and create robust RESTful APIs for seamless integration with other services.
• UI Development (PyQt): Develop or integrate user interfaces for internal tools or customer-facing applications.
• Model Monitoring & Experiment Tracking: Leverage tools like Weights & Biases (wandb) or MLflow to track experiments, monitor model performance, and ensure continuous improvement.
• Performance Optimization: Conduct performance tuning and hardware optimization (GPU/CPU) to achieve high throughput and low latency.
• Collaboration & Mentorship: Work in cross-functional teams (Product, Data, DevOps) and mentor junior developers on best practices and new technologies.

Required Qualifications

• 5+ years of hands-on experience in Computer Vision and Deep Learning.
• Fluency in Python; additional programming languages (C++, Java, etc.) are a plus.
• Expertise in Deep Learning Frameworks: PyTorch and TensorFlow.
• Proficiency with Docker for containerization and microservices.
• Experience with RESTful API design and implementation.
• Knowledge of PyQt (or similar frameworks) for desktop UI development.
• Familiarity with Model Monitoring & Experiment Tracking (Weights & Biases, MLflow, etc.).
• Strong background in linear algebra, calculus, and probability/statistics as they relate to ML.
• Excellent problem-solving and debugging skills.
• Bachelor’s/Master’s/PhD in Computer Science, Electrical Engineering, or a related field (or equivalent work experience).

Preferred Skills & Nice-to-Haves

• Experience with DevOps practices (CI/CD, Kubernetes).
• Familiarity with Cloud Platforms (AWS, Azure, GCP) for model deployment and scaling.
• Understanding of Edge Computing and on-device model optimization (TensorRT, ONNX).
• Knowledge of NVIDIA CUDA for GPU acceleration.
• WANDB and MLflow for training monitoring.
• Contributions to open-source computer vision or deep learning projects. 

What We Offer

• Competitive Compensation
• Flexible Work Arrangements (Remote) and a positive work-life balance.
• Growth Opportunities: A chance to lead cutting-edge projects and mentor junior developers.
• Collaborative Culture: Work alongside passionate professionals in an environment that values innovation and continuous learning.