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.


  • 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.


  • 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
  • Proficient in scripting languages (e.g., Python, Bash) and automation tools (e.g., Ansible,
  • Understanding of machine learning concepts, model training, evaluation metrics, and data
  • Excellent communication skills and ability to work collaboratively in cross-functional teams.
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