SRE LLM Engineer

Role: SRE ML focus

Location - Sunnyvale/Austin

Mandatory Employer Information for Every Submission

Vendors must provide the following employer details:

• Full Company Name
• Company Revenue
• Total Employee Headcount
• Complete Business Address (as per USCIS records)
• Owner Name (as per USCIS documentation)
• Owner Email Address
• Owner Mobile Number
• Owner's LinkedIn Profile

JOB Details:

Responsibilities

• Design and implement cloud solutions, build MLOps on cloud (AWS or GCP)
• 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 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 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