Key Responsibilities

• Collaborate with data scientists and engineers to design, implement, and maintain AI/ML infrastructure.
• Build and manage end-to-end deployment pipelines for machine learning models on Databricks and cloud platforms (GCP preferred).
• Work on scaling and optimizing big data technologies such as Spark, BigQuery, and Databricks environments.
• Implement continuous integration/continuous deployment (CI/CD) best practices for ML workflows and model deployment.
• Ensure seamless integration of machine learning models into production environments, focusing on performance and reliability.
• Support the deployment and monitoring of AI/ML models at scale in a cloud environment.
• Provide operational support for data pipelines, machine learning models, and model monitoring in production.

Required Qualifications

• 7-10 years of overall IT experience, with a focus on building and managing scalable ML systems.
• 3-5 years of hands-on experience in MLOps and working with the Databricks platform in cloud environments.
• Proven expertise in big data technologies such as Apache Spark, BigQuery, and Databricks.
• Proficient in cloud platforms, with a preference for Google Cloud Platform (GCP).
• Strong understanding of machine learning model lifecycle management, including versioning, training, testing, and deployment.
• Experience with containerization and orchestration tools such as Docker, Kubernetes, and Kubeflow.
• Proficiency in Python and/or Scala, as well as scripting languages like Bash for automation tasks.
• Strong knowledge of version control systems like Git.
• Solid understanding of CI/CD pipelines and tools (e.g., Jenkins, GitLab CI, or CircleCI).
• Knowledge of model monitoring, logging, and model performance tracking tools and techniques.

Preferred Skills

• Experience with MLFlow, TensorFlow, PyTorch, or similar ML frameworks.
• Experience with data engineering pipelines and tools (e.g., Apache Airflow, dbt, etc.).
• Familiarity with security best practices for cloud-based MLOps workflows (e.g., securing model endpoints, data privacy concerns).

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