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We are building a Computer Vision platform that automates the full CV model lifecycle, from data preparation and model training through evaluation, deployment, and monitoring. You will join a small, autonomous team of 3 to 5 engineers (plus interns) working alongside AI/ML engineers and computer vision scientists at ST Engineering. You are the subject matter expert for Computer Vision across the entire lifecycle: data preparation, model training, evaluation, deployment, monitoring, and automation. You will also build CV proof-of-concepts to validate product ideas before we commit to building at scale. Scope: 2D image and video. This role does not cover 3D vision, point clouds, LiDAR, or sensor fusion. What You'll Build Computer Vision Models and Pipelines (Primary Focus) Train, fine-tune, and evaluate CV models for classification, detection, and segmentation on real, imperfect data Own the data side: dataset curation, labeling strategy and quality control, augmentation pipelines, and dataset versioning Define evaluation methodology that reflects the use case (mAP, IoU, precision and recall at the operating point), run error analysis, and build regression tests that stop weak models from shipping Package and deploy models to production (ONNX Runtime, TorchServe, or similar), including inference optimization against latency and throughput targets Monitor deployed models: performance and data drift, failure case capture, and retraining triggers Automation Platform Build backend services (Python, FastAPI) that turn the above into repeatable, automated pipelines the team and the platform can trigger programmatically Wrap CV toolkits (PyTorch, OpenCV, Ultralytics YOLO) so data prep, training, evaluation, and deployment can be orchestrated end to end Design clean, versioned, documented APIs that internal teams and platform services consume Implement orchestration for multi-step pipelines with retry, rollback, and defined SLAs Proof-of-Concepts Rapidly prototype end-to-end Computer Vision applications to validate product ideas before committing to a full build Translate prototype learnings into production architecture decisions Infrastructure and Quality Ship production systems with containerization (Docker, Kubernetes), CI/CD (GitHub Actions), and monitoring (Prometheus, Grafana) Establish engineering quality through testing (pytest), observability, and clean architecture What You Bring Must Have Minimum 4 years building Computer Vision systems, with a mix of end-to-end ownership: you have taken CV models from raw data through training and evaluation into a production deployment that real users or systems depend on, rather than prototypes that stopped at a benchmark Hands-on model work: you have trained, fine-tuned, and evaluated your own CV models (classification, detection, segmentation), not only built infrastructure around models other people trained Data preparation depth: labeling and annotation quality, class imbalance, augmentation strategy, and the data-quality failure modes that quietly degrade model performance Deep PyTorch and OpenCV experience Strong Python with production-grade API development (FastAPI) Model packaging and serving in production (ONNX Runtime, TorchServe, or similar) Deployment and operations: Docker, CI/CD (GitHub Actions), and monitoring of live models Solid system design and architecture skills Nice to Have GPU infrastructure: CUDA and cuDNN management, multi-GPU training on Ubuntu servers Inference optimization beyond ONNX: TensorRT, OpenVINO, Triton, or edge runtimes Kubernetes at production scale Experiment tracking and automated model evaluation (MLflow or Weights & Biases) Classical CV: geometry and calibration, tracking
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.