MLOps Engineer

We have an excellent Job opportunity for MLOps Engineer

So, if you are interested, please share your updated resume on Syed.kausar@talentola.com to discuss further.

Role- MLOps Engineer

Location- Sunnyvale/CA or Austin/TX (Onsite)

Job Description:

Skills:

• 6+ years of experience in ML Ops with strong knowledge in Kubernetes, Python and AWS
• Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent)
• Strong software engineering skills in complex, multi-language systems
• Fluency in Python
• Comfort with Linux administration
• Ability to understand tools used by data scientist 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, MS Azure or GCP)
• 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 with 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
• Fluent in English, good communication skills and ability to work in a team

Responsibilities:

• Design and implement cloud solutions, build MLOps on cloud (AWS, Azure, or GCP)
• Build CI/CD pipelines orchestration by GitLab CI, GitHub Actions, Circle CI, Airflow or similar tools
• 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 architect, document the processes
• Develop and deploy scalable tools and services for our clients to handle machine learning training and inference