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
- 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
- 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
To apply for this job email your details to firstname.lastname@example.org