Salla is seeking an accomplished MLOps Engineer to become a member of our team. In the role of MLOps Engineer, you will be tasked with the design, implementation, and maintenance of our machine learning infrastructure.
You will collaborate closely with our machine learning engineers and data scientists to ensure the efficient and seamless operation of our machine learning models. The ideal candidate will possess a robust background in computer science, machine learning, software engineering, as well as expertise in the design and implementation of cloud systems. The candidate should also have experience with MLOps frameworks such as Kubeflow, MLFlow, DataRobot, and Airflow, in addition to familiarity with containerization technologies like Docker and Kubernetes.
Responsibilities
- Design and implement cloud solutions for machine learning infrastructure, build MLOps on cloud (AWS, Azure, or GCP).
- Build CI/CD pipelines orchestration by Jenkins CI, GitHub Actions, Circle CI, Airflow or similar tools.
- Collaborate with machine learning engineers to deploy machine learning models, and document the processes.
- Data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality.
- Data science models testing, validation and tests automation.
- Build custom integrations between cloud-based systems using APIs.
- Build and maintain automated data pipelines.
- Support model development, with an emphasis on auditability, versioning, and data security.
- Facilitate the development and deployment of proof-of-concept machine learning systems.
- Implement and maintain monitoring and logging systems.
- Develop automation tools to improve the efficiency of processes.
- Ensure our machine learning infrastructure is reliable, scalable, and secure.
- Troubleshoot and resolve infrastructure issues.
Requirements
- Bachelor's degree in Computer Science, Mathematics, or a related field.
- 3+ years of experience as a MLOps Engineer or similar role.
- Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud solutions (AWS, MS Azure or GCP).
- Experience with MLOps Frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc., experience with containerization using Docker and Kubernetes.
- Strong knowledge of machine learning techniques and frameworks such as TensorFlow, Keras, and PyTorch.
- Ability to understand tools used by data scientist and experience with software development and test automation.
- Experience and Certified on cloud platforms such as AWS, Azure, or GCP.
- Strong understanding of software testing, benchmarking, and continuous integration.
- Strong programming skills in Python, Java, or C++and good understanding of Linux
- Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer.
- Good experience in SQL and NoSQL databases like Click-house and Elasticsearch, etc.
