ML Ops Engineer

Responsibilities –

·         Design and implement cloud solutions, build MLOps on cloud (AWS or Google Cloud Platform)

·         Build CI/CD pipelines orchestration by GitLab CI, GitHub Actions, Flux, Kustomize, Circle CI, Airflow or similar tools

• Data science model containerization, deployment using docker, VLLM, Kubernetes

·         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

·         Communicate with a team of data scientists, data engineers and architects, document the processes

·         Develop and deploy scalable tools and services for our clients to handle machine learning training and inference

Qualifications:

·         6+ years of experience in ML Ops with strong knowledge in Kubernetes, Python, MongoDB and AWS.

·         Good understanding of Apache SOLR.

·         Proficient with Linux administration.

·         Knowledge of ML models and LLM.

·         Ability to understand tools used by data scientists and experience with software development and test automation

·         Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud solutions (AWS or Google Cloud Platform)

·         Experience working with cloud computing and database systems

·         Experience building custom integrations between cloud-based systems using APIs

·         Experience developing and maintaining ML systems built with open-source tools

·         Experience with MLOps Frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc., experience with Docker and Kubernetes

·         Experience developing containers and Kubernetes in cloud computing environments

·         Familiarity with one or more data-oriented workflow orchestration frameworks (Kubeflow, Airflow, Argo, etc.)

·         Ability to translate business needs to technical requirements

·         Strong understanding of software testing, benchmarking, and continuous integration

·         Exposure to machine learning methodology and best practices

·         Good communication skills and ability to work in a team

Technical skills: