About Ripik. AI:

We are building an AI platform to make manufacturing more competitive. We enable the shop-floor managers and plant teams to take better decisions via an AI-ML-based DIY platform where they can run analytics and get recommendations. Our beta product is live at several leading Indian companies. We are the provider of an AI-based platform for production analytics. We transform how people operate factories across the world. Our mission is to make manufacturing operations more efficient, productive, and sustainable. We build deep tech solutions such as digital twin which are easy to use. Our solutions have helped clients improve their throughput, quality, yield, and energy efficiency. Our products deliver real value - moving KPIs that matter in manufacturing and industrial companies.

Job Description:

We are looking for a hands-on Data Science Manager 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.

Technical Leadership and 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 (YOLO, SAM, Vision Transformers like DINOv2 Swin, Grounding DINO, RF-DETR, and zero-shot/open-vocabulary detection like YOLO-World, CLIP) to translate 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 optimization, quantization (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 and 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 prioritization, 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 and 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 and 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.

Requirements:

- 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), 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 and 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, and 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