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About Ripik.AIRipik.AI is an Accel-backed Applied AI company building computer vision and process-optimisation agents for the world's largest industrial enterprises across steel, cement, aluminium, 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. About Ripik.AIRipik.AI is an Accel-backed Applied AI company building computer vision and process-optimisation agents for the world's largest industrial enterprises across steel, cement, aluminium, 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. We are one of the few Indian AI product start-ups to be a partner to GCP, Azure, and AWS, and the AI partner of choice for CII, ICC, and NASSCOM. The RoleWe are looking for a hands-on Computer Vision Engineer to work on the models that power Ripik's industrial AI platform. You will own CV problems end-to-end from data strategy and annotation to model development, edge deployment, and production monitoring for some of the most complex vision problems in heavy industry. Key ResponsibilitiesOwn computer vision problems end-to-end 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).Build models for hard 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.Follow and contribute to 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.Debug production issues on live customer deployments trace performance drops, root-cause failure modes, and ship fixes with the right guardrails.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.Build robust evaluation frameworks domain-specific metrics, A/B testing against production baselines, and systematic failure-mode analysis to ensure models deliver real business impact.Partner cross-functionally with product, field engineering, operations, and leadership translate business problems into well-scoped modelling projects and communicate results clearly.What You BringBachelor's in Computer Science, AI/ML, Electrical Engineering, or a related field.13 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).Strong first-principles problem-solving comfortable navigating novel, unstructured problems where no playbook exists.Experience in a high-growth start-up or similarly fast-paced environment.Excellent communication able to distil complex technical concepts for non-technical stakeholders, write clear documentation, and present results to leadership and customers.Good to HaveExperience with industrial or .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.