Experience : 5.00 + years

Salary : Confidential (based on experience)

Expected Notice Period : 7 Days

Shift : (GMT+05:30) Asia/Kolkata (IST)

Opportunity Type : Office ()

Placement Type : Full Time Permanent position(Payroll and Compliance to be managed by: An applied AI company building AI solutions for industrial and manufacturing enterprises.)

**(*Note: This is a requirement for one of Uplers' client - An applied AI company building AI solutions for industrial and manufacturing enterprises.)

**What do you need for this opportunity?

Must have skills required: vision–language models, zero-shot and open-vocabulary detection, Albumentations, and CNN backbones (ResNet, and image / video processing fundamentals., ConvNeXt, Detr, EfficientNet, Python and PyTorch; strong working knowledge of OpenCV, RF-DETR), segmentation models (SAM / SAM 2), transformer-based detectors (RT-DETR, Vision Transformers)., YOLO (v8 / v11 / v26)

An applied AI company building AI solutions for industrial and manufacturing enterprises. is Looking for: The Role**** 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 workplace 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 our 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.
- 5–9 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
- International exposure.

How to apply for this opportunity?

- Step 1: Click On Apply! And Register or Login on our portal.
- Step 2: Complete the Screening Form & Upload updated Resume
- Step 3: Increase your chances to get shortlisted & meet the client for the Interview!

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So, if you are ready for a new challenge, a great work environment, and an opportunity to take your career to the next level, don't hesitate to apply today. We are waiting for you!

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