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
