Machine Learning Engineer

Optomi, in partnership with a leading retail company, is seeking a ML Engineer to join their team! This role is crucial for supporting the migration of AI and ML models from the analytics phase to a full-scale IT environment. The successful candidate will work closely with data science partners to productionize pricing models, ensuring they are scalable, secure, and integrated with IT systems. This involves building data engineering pipelines and utilizing a tech stack including Airflow, Vertex AI, and Kubernetes. The position requires a strong foundation in SQL and Python, with a collaborative mindset to work across functions.

Required Qualifications:

• Strong Python programming and software engineering skills.
• Hands-on MLOps experience with production ML pipelines.
• Experience building CI/CD pipelines and working with Git/GitHub.
• Strong understanding of ML model lifecycle management, deployment, monitoring, and automation.
• Experience with REST APIs/microservices and containerization such as Docker.
• Ability to work effectively with Data Science and Data Engineering teams.
• Experience with GCP, preferably Vertex AI, BigQuery, Cloud Storage, and Composer/Airflow.

Responsibilities:

• Design, build, and maintain scalable MLOps pipelines for ML model development, deployment, and monitoring.
• Develop CI/CD pipelines to automate model and application deployment across environments.
• Build and manage data and ML workflows using tools such as Python, Airflow/Composer, Git, and cloud platforms.
• Deploy and manage ML models using platforms such as Vertex AI or equivalent ML platforms.
• Implement model monitoring, performance tracking, logging, alerting, and retraining workflows.
• Work closely with Data Scientists, Data Engineers, and Software Engineers to productionize ML models.
• Develop APIs and services to integrate ML models with downstream applications.
• Ensure solutions are scalable, reliable, secure, and maintainable.
• Troubleshoot production issues and optimize ML pipelines for performance and reliability.
• Follow software engineering best practices including code reviews, testing, documentation, and version control.