The MLOps Engineer will play a critical role in designing, implementing, and managing scalable Machine Learning (ML) workflows in production environments. The role focuses on automating model deployment, monitoring, and governance within Azure and AWS ecosystems, ensuring operational efficiency, performance, and scalability. The ideal candidate will collaborate with data scientists, engineers, and DevOps teams to create a robust and secure ML lifecycle management system.
Experience:
- 6+ years of experience in MLOps, DevOps, or ML engineering roles.
- Proven expertise in deploying and managing ML models in cloud environments such as Azure ML and AWS SageMaker.
- Proficiency in programming languages like Python and experience with ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
- Hands-on experience with CI/CD tools like Jenkins, Azure DevOps, or GitHub Actions.
Required Skills:
- Expertise in ML lifecycle management, including training, testing, deployment, and monitoring.
- Proficiency in Kubernetes for container orchestration and serverless compute solutions.
- Experience with feature engineering tools like Databricks Feature Store or TFX.
- Strong knowledge of security, compliance, and governance in ML workflows.
- Familiarity with big data tools like Apache Spark or Kafka.
Soft Skills:
- Strategic thinking with the ability to foresee potential challenges.
- Strong collaboration and communication skills to work across diverse teams.
- Proactive, detail-oriented, and focused on delivering high-quality solutions.
- Adaptability to rapidly changing technologies and requirements.
Education:
- Bachelor’s or master’s degree in computer science, Data Science, Artificial Intelligence, or related fields.