Position Overview: 

We are seeking a highly skilled MLOps Engineer with a strong background in Machine Learning (ML) projects to join our team. As an MLOps Engineer, you will play a crucial role in developing, implementing, and maintaining the infrastructure and processes that enable seamless integration, deployment, and management of ML models. The ideal candidate should possess a deep understanding of ML concepts, strong technical expertise, and hands-on experience in building and optimizing MLOps pipelines.


  • MLOps Pipeline Development: Design, develop, and optimize end-to-end MLOps pipelines for ML projects, encompassing model training, testing, validation, and deployment.
  • Model Deployment: Implement efficient and reliable methods for deploying ML models into
    production environments, ensuring scalability, performance, and robustness.
  • Automation and Orchestration: Drive automation efforts to streamline ML workflows, including
    data preprocessing, model training, hyperparameter tuning, and evaluation.
  • CI/CD Integration: Integrate MLOps pipelines with continuous integration and continuous
    deployment (CI/CD) systems, enabling automated model updates and versioning.
  • Monitoring and Logging: Establish monitoring and logging solutions to track model
    performance, data drift, and system health, ensuring prompt identification of issues.
  • Infrastructure Management: Collaborate with DevOps and cloud engineering teams to manage
    and optimize ML infrastructure, leveraging cloud services and containerization technologies.
  • Model Version Control: Implement version control and model registry systems to maintain
    trackability, reproducibility, and model lineage.
  • Collaboration with Data Scientists: Work closely with data scientists and ML engineers to
    operationalize models, address challenges, and optimize model performance.
  • Security and Compliance: Ensure data privacy and compliance with relevant regulations by
    implementing security best practices in ML pipelines.
  • Research and Evaluation: Stay updated with the latest MLOps tools and methodologies,
    conduct evaluations, and recommend improvements 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 an MLOps Engineer, with a focus on deploying and managing ML projects.
  • Strong understanding of ML concepts, algorithms, and techniques, along with experience in model training and evaluation.
  • Proficiency in programming languages such as Python or R, and experience with data manipulation and ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
  • Hands-on experience with MLOps tools and platforms like Kubeflow, MLFlow, or similar solutions
    for managing ML workflows.
  • Familiarity with cloud platforms like AWS, Azure, or GCP, and experience with managing
    cloud-based infrastructure.
  • Knowledge of containerization technologies like Docker and container orchestration with
    Kubernetes is a plus.
  • Understanding of DevOps principles, CI/CD pipelines, and version control systems (e.g., Git).
  • Strong analytical and problem-solving abilities to address complex MLOps challenges and drive
    continuous improvement.
  • Excellent teamwork and communication skills to work effectively with cross-functional teams and
    communicate technical concepts to non-technical stakeholders.
  • Experience setting up MLOps workflows using platforms like Kubeflow/ KServe
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