• Oversee ML bot research, development, and implementation. • Identify and evaluate opportunities to use generative AI across the production pipeline. • Gather AI-related ideas and needs from teams and convert them into actionable plans. • Drive execution of approved AI initiatives, prototypes, and workflow improvements. • Explore and prototype local models trained on internal data. • Develop guidelines and best practices for agentic AI systems (e.g., Windwalk, Antigravity). • Educate directors and teams on AI capabilities, limitations, and practical usage. • Up to date knowledge with emerging AI models, tools, and industry trends. • Understand major AI models and clearly explain their strengths, weaknesses, and applicability

• Build and improve ML components across data, training, evaluation, and inference. • Fine-tune and adapt models as part of larger production systems. • Implement evaluation and testing to understand model behavior. • Help build and maintain data pipelines for real-world and synthetic data. • Debug model issues, performance problems, and production incidents. • Ship improvements iteratively and learn from real user feedback. • Work closely with senior ML engineers and product teams. • Work under real production constraints: latency, cost, reliability, and safety

• Build and improve ML components across data, training, evaluation, and inference. • Fine-tune and adapt models as part of larger production systems. • Implement evaluation and testing to understand model behavior. • Help build and maintain data pipelines for real-world and synthetic data. • Debug model issues, performance problems, and production incidents. • Ship improvements iteratively and learn from real user feedback. • Work closely with senior ML engineers and product teams. • Work under real production constraints: latency, cost, reliability, and safety

• Design, build, and maintain end-to-end MLOps pipelines using AWS SageMaker • Develop and manage Airflow DAGs for ML workflow orchestration (training, validation, deployment, retraining) • Automate model training, evaluation, versioning, and deployment • Implement CI/CD pipelines for ML workflows and model releases • Manage model lifecycle, including experimentation, deployment, monitoring, and retraining • Integrate data ingestion and feature engineering workflows with ML pipelines • Monitor model performance, data drift, and pipeline reliability • Collaborate closely with Data Scientists, Data Engineers, and DevOps teams • Ensure security, scalability, and cost optimization across ML infrastructure