Architect and establish cloud infrastructure and data workflows to facilitate the deployment of large-scale machine learning models in production environments.
Define and promote best practices for MLOps to ensure high standards of quality, consistency, and automation across the organization.
Innovate and implement continuous integration and delivery (CI/CD) pipelines, enabling swift iteration and deployment of ML models and systems.
Collaborate with cross-functional teams to identify, create, and integrate tools and services optimally supporting ML processes, from training and tuning to inference.
Keep abreast of emerging technologies and propose integration strategies to enhance ML system performance, maintainability, and reliability.
Contribute to ML systems security, traceability, versioning, and automate the deployment of proof-of-concept projects to production.
Job Requirements:
Minimum of 3 years of experience in building and deploying end-to-end machine learning projects in a similar role as an ML Ops Engineer, ML Platform Engineer, or ML Engineer.
Proficient knowledge of popular machine learning frameworks, including PyTorch, Tensorflow, and associated technologies.
Profound software engineering skills, with a strong command of Python and cloud computing environments.
Solid understanding of cloud security principles and compliance standards within platforms such as AWS and GCP.
Expertise in scalable database systems and proficient experience in developing CI/CD pipelines in cloud-based architectures.
Proven experience in containerization technologies and orchestration tools like Kubernetes and Terraform.
Demonstrated ability to develop custom API integrations and familiarity with data orchestration frameworks.
Autonomous and innovative problem-solving skills, with the ability to lead, impactful initiatives in a dynamic and uncertain landscape.