Position Overview:
We are looking for an experienced DevOps Engineer with a strong background in Machine Learning (ML) projects and a deep understanding of MLOPS (Machine Learning Operations) tools like MLFlow and Tecton. As a key member of our DevOps team, you will play a critical role in bridging the gap between data science and engineering by developing, maintaining, and optimizing the infrastructure and processes required for successful ML deployments. The ideal candidate should possess a passion for driving automation, continuous integration/continuous deployment (CI/CD), and implementing MLOps best practices to enable seamless ML model management.
Responsibilities:
- MLOps Implementation: Design, build, and maintain end-to-end MLOPS pipelines and
frameworks, integrating tools such as MLFlow, Tecton, and others to support the deployment, monitoring, and management of ML models. - CI/CD Automation: Develop and enhance CI/CD pipelines for ML projects to enable automated
and rapid model training, evaluation, and deployment. - Infrastructure Orchestration: Architect, configure, and manage the infrastructure required for ML projects, including containerized environments (e.g., Docker, Kubernetes) and cloud services
(e.g., AWS, Azure, GCP). - Version Control and Model Registry: Implement version control practices for ML models and
establish model registries to maintain trackability and reproducibility. - Monitoring and Logging: Set up monitoring and logging systems for ML model performance,
data drift, and system health to ensure smooth functioning and timely identification of issues. - Collaboration with Data Scientists: Collaborate with data scientists and ML engineers to
understand model requirements, operationalize models, and improve overall model performance
and efficiency. - Security and Compliance: Implement security best practices to ensure data privacy and
compliance with relevant regulations in the ML environment. - Scalability and Efficiency: Optimize infrastructure and processes for scalability,
cost-effectiveness, and high availability to handle large-scale ML workloads. - Tool Evaluation: Stay abreast of the latest MLOPS tools and technologies, conduct evaluations,
and make recommendations for their adoption to enhance the ML engineering workflow.
Qualifications:
- Bachelor’s or higher degree in Computer Science, Engineering, or a related field.
- Minimum of 5 years of relevant experience as a DevOps Engineer with a focus on ML projects
and experience with MLOPS tools like MLFlow and Tecton. - In-depth understanding and hands-on experience with MLOPS tools such as MLFlow, Tecton,
Kubeflow, or similar platforms. - Strong knowledge of DevOps principles, CI/CD pipelines, and experience with tools like Jenkins, GitLab CI, or CircleCI.
- Familiarity with cloud platforms such as AWS, Azure, or GCP and experience with managing
cloud-based infrastructure. - Proficiency in containerization technologies like Docker and container orchestration tools like
Kubernetes. - Proficient in scripting languages (e.g., Python, Bash) and automation tools (e.g., Ansible,
Terraform). - Understanding of machine learning concepts, model training, evaluation metrics, and data
preprocessing. - Excellent communication skills and ability to work collaboratively in cross-functional teams.