Primary responsibilities: 

• Develop and manage deployment processes for machine learning models, ensuring seamless integration into production environments

• Design and implement automated CI/CD pipelines for ML workflows, adhering to company standards and best practices

• Create and maintain monitoring tools to track model performance, reliability, and accuracy in production

• Optimize infrastructure for model training, testing, and deployment, including the development of template scripts and automation to accelerate the development process

• Collaborate with data scientists, data engineers, and platform engineers to streamline ML operations and integrate new AI technologies into the platform ecosystem

• Ensure security and compliance of ML models and workflows with industry standards, regulations, and company governance frameworks

• Research and integrate best practices and new technologies in MLOps to improve efficiency and effectiveness

• Assist in the creation and implementation of rigorous evaluation and validation processes for ML models, focusing on automation of validation scripts for deployment

• Contribute to the development and maintenance of training materials and user guides for the AI platform

Experience and Skills Required:

• Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field

• 6+ years of experience in software engineering or DevOps, including at least 2-3 years of hands-on experience with machine learning operations or AI platform engineering

• Demonstrated experience in deploying and maintaining machine learning models in production environments

• Strong programming skills in Python and proficiency with shell scripting

• Extensive experience with CI/CD tools (e.g., Jenkins, GitLab CI, or Azure DevOps)

• In-depth knowledge of containerization technologies (e.g., Docker) and orchestration platforms (e.g., Kubernetes)

• Familiarity with cloud platforms (e.g., AWS, Azure, or GCP) and their ML-specific services

• Practical experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn

• Strong understanding of data pipelines, ETL processes, and data storage solutions

• Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack)

• Excellent problem-solving skills and ability to optimize complex systems

• Strong communication skills and ability to work effectively in a collaborative environment

• Knowledge of data governance, security best practices, and compliance regulations related to AI/ML

• Experience with version control systems (e.g., Git) and ML model versioning tools (e.g., MLflow, DVC)