MLOps Engineer

We are seeking a highly skilled and experienced MLOps Engineer to join our AI Platform team.
In this role, you will be responsible for developing, implementing, and maintaining robust MLOps processes and infrastructure to support our machine learning initiatives.
You will work closely with data scientists, platform engineers, and other stakeholders to streamline the deployment and management of ML models in production environments.
The ideal candidate will have a strong background in both machine learning and DevOps practices, with a focus on automation, scalability, and reliability..
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).