• Lead the end-to-end machine learning lifecycle, including data ingestion, model development, training, evaluation, deployment, monitoring, and iteration • Own and evolve ML Ops architecture, including CI/CD for models, feature pipelines, model versioning, monitoring, and retraining strategies • Serve as a player-coach, contributing directly to design reviews, code reviews, prototyping, and implementation for high-impact initiatives • Architect and deploy computer vision models (e.g., CNNs, transformers, multimodal models) and LLM-based systems (e.g., prompt pipelines, fine-tuning, RAG, evaluation frameworks) • Establish and track model performance, reliability, scalability, and ethical AI metrics, ensuring compliance with internal standards and regulatory expectations • Lead project planning, feasibility analysis, and technical scoping for new AI-driven functionality • Identify technical risks early and develop mitigation and contingency plans • Maintain and design software primarily in Python, with the ability to contribute to or review code in C/C++, Java, JavaScript, C#, and mobile frameworks as needed • Ensure strong testing discipline, including unit testing, integration testing, and test-driven development (TDD) practices • Apply and mentor on software design patterns, debugging strategies, and production troubleshooting best practices • Collaborate cross-functionally to align priorities, manage dependencies, and communicate progress, tradeoffs, and outcomes • Write and present technical and executive-level status updates to stakeholders • Oversee software and model releases and communicate release readiness and impact