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Take your passion to the next level and work alongside other masters of their craft to build a fulfilling and rewarding career at Marina Bay Sands.
Job Summary
You will participate in end-to-end computer vision solutioning—from data strategy and model selection to validation, deployment, and monitoring. Beyond CV, you’ll contribute broader AI/ML capabilities where valuable. Your focus: build reliable models that generalize in production, with strong data practices and measurable outcomes.
Job Responsibilities
- Own CV model lifecycle: Problem framing, dataset design/labeling strategy, model selection, training, hyperparameter tuning, evaluation, and deployment.
- Data sanitization & engineering: Create pipelines for data quality (de-duplication, balance, augmentation), privacy/compliance, and annotation workflows; design versioned datasets.
- Model validation & monitoring: Define metrics (mAP/F1/ROC-AUC/latency), implement drift/shift detection, set acceptance gates, and maintain post-deploy monitoring loops.
- MLOps excellence: Build reproducible training, experiment tracking, artifact management, and automated model promotion (staging → prod) with rollback.
- Cross-functional delivery: Partner with software engineers to translate ambiguous goals into deployable CV solutions that fit live workflows.
- Knowledge sharing & governance: Author technical docs; set standards for labeling, bias checks, and reproducibility.
Education & Certification
- Bachelor’s degree in Electrical Engineering, Mechanical Engineering, Computer Science, or related field preferred
- 4–8 years in AI/ML with a strong emphasis on computer vision (classification, detection, tracking, or video analytics).
- Proficiency in Python and deep learning frameworks (PyTorch strongly preferred; TensorFlow acceptable).
- Solid understanding of statistics and ML fundamentals (sampling, hypothesis testing, regularization, cross‑validation).
- Experience deploying models to production (batch and real-time) and monitoring performance.
- Hands-on with data versioning, experiment tracking, and model evaluation at scale.
- Experience with Azure ML, AKS, Azure Functions, Blob Storage, Event Hub.
- Knowledge of edge inference (NVIDIA Jetson, ONNX/TensorRT), or Triton Inference Server.
- Broader AI/ML skills (NLP, recommendation, time-series forecasting) and data analytics (SQL, BI).
- Familiarity with QA/QC for ML: bias/fairness checks, robustness testing, adversarial examples, test oracles for CV.
- Experience in hospitality/F&B/gaming use cases and privacy/compliance considerations.
Key Skills
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