ML Ops Engineer

itle: ML Ops Engineer

Location: Austin, TX OR Sunnyvale, CA

Look for candidate with in the time zone

Job Summary

For this role, we are looking for ML Ops Engineer with Kubernetes and Python.

Experience Required:

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

Technical skills:

• Python, Kubernetes, Mongo DB, Microservices, AWS
• SOLR
• ML operations, CI/CD pipelines, LLM
• 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, MS Azure or GCP)

Qualifications:

• 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

Behavioral Skills:

• Excellent Communication skills and collaboration skills
• Ability to propose and implement improvements in the system
• Ability to work with cross-functional stakeholders