ML Engineer - Generative AI

Description :

The ML Engineer will be responsible for developing, deploying, and managing production-grade Machine Learning, AI, and Generative AI models on the Snowflake platform.

The role involves end-to-end ownership of ML pipelines, from model training and versioning to deployment and monitoring.

The ideal candidate will have strong experience in Python programming, SQL optimization, MLOps practices, and containerized deployments.

This position requires close collaboration with data engineering, analytics, and DevOps teams to ensure efficient model integration, automation, and scalability in a cloud-based environment.

Key Responsibilities :

• Design, build, and deploy ML, AI, and GenAI models within Snowflakes data and compute ecosystem.
• Develop automated data pipelines and workflows to support continuous model training and deployment.
• Implement CI / CD and MLOps best practices using tools such as MLflow, Git, and cloud-based DevOps pipelines.
• Manage model lifecycle, including version control, experiment tracking, and performance monitoring.
• Integrate and operationalize models within production systems through APIs and scheduled jobs.
• Work with data teams to optimize SQL queries, ensure data consistency, and manage model input pipelines.
• Implement Docker-based model deployment workflows and ensure reproducibility across environments.
• Collaborate with cross-functional teams to streamline data access, testing, and release management.
• Ensure scalability, performance, and reliability of deployed models within Snowflake and connected systems.
• Maintain documentation, performance dashboards, and deployment logs to ensure traceability and 4 to 8 years of experience in machine learning engineering, data engineering, or AI solution deployment.
• Hands-on experience deploying ML and GenAI models on Snowflake or equivalent data platforms.
• Strong programming skills in Python with experience in model training, testing, and inference.
• Proficiency in SQL for data extraction, transformation, and analysis within enterprise databases.
• Experience in MLflow for experiment tracking, model registry, and deployment orchestration.
• Working knowledge of CI / CD pipelines, version control, and DevOps tools such as Jenkins, GitLab, or Azure DevOps.
• Practical exposure to containerization and deployment using Docker.
• Understanding of MLOps principles including model retraining, drift detection, and monitoring.
• Ability to work in cross-functional teams and deliver high-quality, production-ready solutions

(ref : hirist.tech)