Job Summary
We are seeking a Senior MLOps Engineer to lead the development, deployment, and management of machine learning systems and infrastructure. This role requires a blend of expertise in machine learning, cloud platforms, and DevOps practices. You will work closely with data scientists and engineers to ensure the smooth operation and scalability of ML models.
Responsibilities
- Design, implement, and manage scalable deployment pipelines for machine learning models using tools like Kubernetes, Docker, and cloud services (AWS, Azure, GCP).
- Build and maintain robust infrastructure for model training, serving, and monitoring. Ensure systems are reliable, secure, and cost-efficient.
- Optimize end-to-end ML workflows and pipelines, including data preprocessing, model training, and deployment. Implement automated CI/CD processes for ML models.
- Work with data scientists to understand model requirements and provide support for integrating models into production systems. Collaborate with engineering teams to align on infrastructure needs.
- Develop and implement monitoring and logging solutions to track model performance, system health, and operational metrics. Address issues and perform troubleshooting as needed.
- Ensure ML systems adhere to security standards and best practices. Implement access controls, data protection measures, and compliance with relevant regulations.
- Create and maintain detailed documentation for ML systems, processes, and best practices. Share knowledge with team members and contribute to the development of internal guidelines.
- Stay current with the latest developments in MLOps, machine learning technologies, and cloud platforms. Evaluate and integrate new tools and techniques to enhance ML operations.
Requirements
- 8+ years of experience in MLOps or a related field, with a proven track record of deploying and managing ML models in production environments.
- Proficiency in ML frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn), containerization tools (Docker, Kubernetes), and cloud platforms (AWS, Azure, GCP). Experience with DevOps tools and practices.
- Strong programming skills in Python and/or other relevant languages.
- Deep understanding of ML systems architecture, data pipelines, and model deployment strategies.
- Knowledge of container orchestration and infrastructure as code (IaC) tools (e.g., Terraform, Ansible).
- Excellent analytical and problem-solving skills, with the ability to troubleshoot complex issues and develop effective solutions.
- Strong communication skills, with the ability to collaborate with cross-functional teams and explain technical concepts to non-technical stakeholders.
- Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field. Advanced degree is a plus.
We regret to inform that only shortlisted candidates will be notified / contacted.
Allegis Group Singapore Pte Ltd, Company Reg No. 200909448N, EA Licence No. 10C4544
