Ripik AI - Computer Vision Manager

Overview about Ripik.AI : Ripik.ai is an Accel-backed Applied AI company building computer vision and process-optimization agents for the world's largest industrial enterprises across steel, cement, aluminum, chemicals, pharma, and power. Our Vision AI platform acts as an automated pair of eyes on the shop floor monitoring materials, equipment, and processes 24/7 with 95%+ accuracy, eliminating human error, and delivering measurable gains in throughput, yield, energy efficiency, and safety. We work with marquee customers including Tata Steel, JSW, ArcelorMittal, Vedanta, Godrej & Boyce, Grasim, Holcim, and Jindal Steel, and are scaling globally across India, the Middle East, Europe, and North America. As we move into our next phase of growth, we are building the operating backbone that will take Ripik from a high-velocity scale-up to a category-defining global industrial AI company. The Role : We are looking for a hands-on Data Science Manager to lead a team of 6 to 8 AI - Computer Vision engineers. This is a player-coach role : you will set technical direction, personally solve the hardest modelling problems, and grow a high-performing engineering team all within the fast-moving environment of a venture-backed industrial AI start-up. Prior formal management experience is not required; what matters is deep technical expertise, the ability to influence through craft, and the drive to build and ship in ambiguous, high-stakes settings. Key Responsibilities : 1. Technical Leadership & Hands-on Delivery : - Own the end-to-end computer vision roadmap from problem framing and data strategy through model development, edge deployment, and production monitoring across Ripiks industrial portfolio (steel, cement, pharma, paints, and beyond). - Personally architect and build solutions for the most complex vision challenges : novel defect types, extreme class imbalance, multi-camera fusion, low-light / high-noise factory environments, and real-time inference on constrained edge hardware. - Stay at the cutting edge of CV research and rapidly evaluate and adopt new models and techniques YOLO26, SAM 3, Vision Transformers (DINOv2, Swin), Grounding DINO, RF-DETR, zero-shot / open-vocabulary detection (YOLO-World, CLIP) translating papers into production value. - Define and enforce engineering standards for the vision stack : model training pipelines, data versioning (DVC), annotation workflows (CVAT, Roboflow, Label Studio), experiment tracking (W&B, MLflow), edge export formats (TensorRT, ONNX, OpenVINO), and CI/CD for model updates. - Drive inference optimisation quantisation (INT8 / FP16, GPTQ), pruning, knowledge distillation, and batching strategies to meet latency and cost targets across NVIDIA Jetson, industrial PCs, and cloud GPU instances. 2. Team Building & People Growth : - Lead, mentor, and grow a team of 68 Computer Vision engineers set clear goals, run structured code reviews and design reviews, and create an environment of rapid learning and ownership. - Hire and onboard strong engineers; raise the technical bar through hands-on pairing, knowledge-sharing sessions, and a culture of experimentation over perfection. - Manage sprint planning, task prioritisation, and delivery timelines; balance exploratory R&D with committed product deliverables in a fast-paced start-up cadence. - Act as the primary technical interface between the CV team and cross-functional stakeholders product, field engineering, operations, and leadership translating business problems into well-scoped modelling projects and communicating results clearly. 3. Innovation & Problem Solving : - Identify and frame novel, first-of-its-kind vision problems in industrial settings where off-the-shelf approaches fall short; design creative solutions combining classical image processing, deep learning, and domain heuristics. - Champion a data-centric AI approach invest in annotation quality, active learning, synthetic data generation, and feedback loops from production rather than only chasing bigger models. - Establish robust evaluation frameworks : domain-specific metrics, A/B testing against production baselines, and systematic failure-mode analysis to ensure models deliver real business impact. Required Skills & Experience : - Bachelors or Masters degree (or PhD) in Computer Science, AI/ML, Electrical Engineering, or a related field. - 5 to 8 years of hands-on experience in computer vision with a strong track record of taking models from research / prototyping through to production deployment. - Deep proficiency in Python and PyTorch; strong working knowledge of OpenCV, Albumentations, and image / video processing fundamentals. - Demonstrated expertise across multiple CV tasks : object detection, instance / semantic / panoptic segmentation, anomaly detection, pose estimation, or tracking. - Hands-on experience with modern model families YOLO (v8 / v11 / v26), transformer-based detectors (RT-DETR, DETR Ov