Computer Vision & Machine Learning Engineer

Project delivery:

• Own and deliver end-to-end computer vision projects focused on equipment defect detection, thermal anomaly identification, vegetation encroachment monitoring, and surveillance of closed areas.
• Scope, plan, and execute projects from problem framing through production deployment and monitoring.
• Deliver on client projects, translating requirements and raw data into working computer vision solutions.

Research and experimentation:

• Stay current with ML/CV research, identify promising methods, and evaluate their applicability to power grid analysis.
• Adapt and implement algorithms from papers, validating against baselines and benchmarking for production viability.
• Design and execute experiments with systematic hyperparameter tuning, ablation studies, and appropriate baselines.

Engineering and production:

• Develop production-grade Python libraries for the complete ML lifecycle, including data pipelines, model serving, and monitoring.
• Own experiment tracking and model versioning with configurations, seeds, dataset versions, and checkpoints.
• Build model serving pipelines that meet latency and throughput requirements, and conduct thorough code reviews.