SRE ML focus

Role:
SRE ML focus

Rate: $70/hr

Location - Sunnyvale/Austin

• Design and implement cloud solutions, build MLOps on cloud (AWS or GCP)
• Build CI/CD pipelines orchestration by Git Lab CI, Git Hub 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

• 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, Data Robot, 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

Skill Area

Includes

Weight (%)

Platform Reliability & Containerization

Kubernetes, Docker, Microservices, Linux

30%

MLOps & AWS Cloud

Model deployment, versioning, monitoring, AWS (Sage Maker, S3, Lambda, EKS)

25%

CI/CD & Git Ops

Git Hub Actions, Flux

15%

Monitoring & Observability

Splunk, Grafana, Prometheus, performance tracking

15%

Integration & Collaboration

Python scripting, MongoDB, API integrations, Apache Solr, LLM awareness, teamwork with data scientists & engineers

15%