Computer Vision Lead

Overview About Ripik.AI Ripik.ai is a fast-growing industrial AI SaaS start-up founded by IIT D / BITS alumni with extensive experience at McKinsey, IBM, Google, and others. It is backed by marquee VC funds like Accel, Venture Highway and 25+ illustrious angels including 14 unicorn founders. Ripik.ai builds patented full-stack software for automation of decision making on the factory floor. Today, we are deployed at more than 15 of the largest and most prestigious enterprises in India including the market leaders in steel, aluminium, cement, pharma, paints, consumer goods and others. It is one of India’s very few AI product start-ups to be a partner to GCP, Azure, and AWS. We are also the AI partner of choice for CII, ICC, and NASSCOM. The Role We are looking for a hands-on Computer Vision Lead to lead a team of 6–8 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 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 Ripik’s 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. Team Building & People Growth
• Lead, mentor, and grow a team of 6–8 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. 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

• Bachelor’s or Master’s degree in Computer Science, AI/ML, Electrical Engineering, or a related field.
• 4–8 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, RF-DETR), segmentation models (SAM / SAM 2), and CNN backbones (ResNet, EfficientNet, ConvNeXt, Vision Transformers).
• Production experience deploying models to edge or on-prem hardware using TensorRT, ONNX Runtime, or OpenVINO; comfort with Docker, Kubernetes, and at least one cloud platform (AWS / Azure / GCP).
• Experience in a high-growth start-up or similarly fast-paced environment where scope is ambiguous, timelines are tight, and wearing multiple hats is the norm.
• Strong first-principles problem-solving ability — comfortable navigating novel, unstructured problems where no playbook exists.
• Excellent communication skills — able to distil complex technical concepts for non-technical stakeholders, write clear documentation, and present results to leadership and customers. Good to Have
• Prior experience leading or mentoring a small engineering team (formal management title not required; tech-lead, senior IC, or project-lead experience counts).
• Experience with industrial or manufacturing domains — understanding of factory-floor constraints, camera setups, lighting variability, and integration with PLCs / SCADA systems.
• Familiarity with zero-shot and open-vocabulary detection (Grounding DINO, YOLO-World, CLIP) and foundation models (DINOv2, SAM 3, Florence) for data-efficient learning.
• Exposure to vision–language models (GPT-4o vision, Gemini, LLaVA) for combining visual inspection with natural-language reporting or operator copilots.
• Knowledge of 3D vision, depth estimation, point-cloud processing, or multi-camera calibration for volumetric industrial inspection.
• Experience with multi-object tracking (ByteTrack, BoT-SORT) and video analytics pipelines for continuous production-line monitoring.
• Contributions to open-source CV projects, publications in top-tier venues (CVPR, ECCV, ICCV, NeurIPS), or strong Kaggle competition results. What Can You Expect?
• A leadership seat at a high-growth, venture-backed AI start-up — directly shape the technical direction and team culture of the computer vision function.
• Ability to shape the future of manufacturing by leveraging best-in-class AI and software; develop a niche skill set at the intersection of deep learning and heavy industry.
• World-class work culture, coaching, and development.
• Mentoring from highly experienced leadership from world-class companies (refer to Ripik.AI website for details).
• International exposure. For More Information
• https://www.youtube.com/watch?v\=YRuz3XxPfcE
• https://www.ripik.ai
• https://www.linkedin.com/company/ripik\-ai/