Job Description:

The Role:

We are looking for a hands-on Data Science Manager to lead a team of 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 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.

2. 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.

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.

- 5-8 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/

Work Location:

NOIDA, Sector-16, (Work from Office)