Role Overview :

We are seeking a versatile ML Engineer with strong expertise in deploying ML, AI, and GenAI models on the Snowflake platform.

The ideal candidate will have hands-on experience with Snowflake Cortex, building and managing ML pipelines, and utilizing tools like MLflow within DevOps and CI/CD environments.

Mastery of Python, SQL, Docker, and Snowpark development is essential to engineer scalable, performant AI/ML workflows.

Exposure to automated code review tools like Linting and Black will further enhance code quality and maintainability.

Key Responsibilities :

- Architect, develop, and deploy machine learning and Generative AI models leveraging Snowflake and its Cortex capabilities.

- Build, automate, and manage robust ML pipelines incorporating tracking, versioning, and model lifecycle management using tools like MLflow.

- Implement DevOps best practices and enable CI/CD pipelines for smooth, automated model deployment and updates.

- Develop reusable components and business logic using Python, SQL, and Snowpark for efficient model training and inference.

- Containerize ML projects with Docker to ensure reproducibility, portability, and scalable production deployments.

- Integrate and enforce automated code review processes using tools such as Linting and Black to maintain high code standards.

- Collaborate with data scientists, engineers, and infrastructure teams to optimize resource usage, model performance, and operational reliability.

- Monitor model health, schedule retraining, and troubleshoot production issues to ensure continuous AI service quality.

Required Skills and Qualifications :

- Strong proficiency in Python, SQL, Docker, and hands-on experience with Snowpark for building data and ML components.

- Experience deploying and managing ML/AI/GenAI models on the Snowflake platform, with knowledge of Snowflake Cortex.

- Familiarity with MLflow or similar model management tools and ability to drive end-to-end ML pipelines.

- Understanding of DevOps practices and CI/CD pipelines tailored to AI/ML workflows.

- Exposure to automated code quality tools such as Linting and Black is highly desirable.

- Strong collaboration, problem-solving skills, and ability to work in complex, fast-paced environments.

Recommended Experience :

- 4 to 8 years of relevant experience in ML engineering, data engineering, or AI product development roles with a focus on cloud-native data platforms