Senior MLOps Engineer (offline)

Intetics Inc., a leading American technology company providing custom software application development, distributed professional teams creation, software product quality assessment, and “all-things-digital” solutions, is looking for a talented Senior MLOps Engineer to join their team.

About the project:

Participation in the development of the cloud- and vendor-agnostic platform that allows us to cater for a wide range of teams, solving all sorts of machine-learning tasks.

This means many data types, frameworks, multiple clouds, etc.

Our platform must support development, deployment, and maintenance across all ML Company use cases. We need to continuously find the proper abstraction levels to make every platform part reusable and agnostic.

Responsibilities:

- Speed up the process from development to deployment to deliver ML solutions faster.

- Develop practices for stable and reliable ML models and infrastructure.

- Build a scalable platform for increasing data, models, and demand.

- Adhere to regulations and internal governance standards.

- Support diverse ML tasks and environments with a cloud- and vendor-agnostic approach.

-Provide end-to-end support for ML development, deployment, and maintenance.

- Focus on innovation and the reusability of platform components.

- Participate in MLOps communities and contribute to open source.

- Utilize agile methods for planning and continuous improvement.

- Ensure alignment and support from all organizational levels.

- Encourage contributions from those eager to tackle novel problems and scale solutions.

Requirements:

- 5+ years of experience in software engineering, focusing on MLOps, DevOps, or related fields.

- Strong experience with cloud platforms such as AWS, Azure, or GCP.

- Experience with Kedro.

- Experience with containerization technologies like Docker and container orchestration platforms like Kubernetes.

- Experience with CI/CD tools and practices, such as Jenkins, GitLab, or CircleCI.

- Familiarity with data science workflows and machine learning frameworks.

- Strong problem-solving skills and the ability to work effectively in a collaborative environment.

- Excellent communication and documentation skills.