This includes writing high-quality, well-tested code; applying strong software engineering practices (version control, code reviews, documentation, modular design, and secure coding); and partnering with platform and engineering teams to implement CI/CD, reproducible training/inference pipelines, model/version governance, performance optimization, monitoring/alerting, and reliable deployment patterns across batch and real-time use cases. Build and maintain production-ready AI/ML services and pipelines by applying best-in-class software engineering practices (clean, modular code; testing; code reviews; documentation; CI/CD), and ensuring robust deployment, monitoring, and ongoing performance/reliability of models in production.