Senior MLOps LLMOps Engineer

Location: Milpitas CA (Hybrid 4 Days Onsite)
Employment Type: Contract
Experience Level: Senior / Lead Engineer

STAFFXPERT LLC is seeking a Senior MLOps / LLMOps Engineer on behalf of our client in Milpitas CA to standardize scale and optimize enterprise ML and GenAI deployment pipelines. This role is ideal for a highly skilled engineer with deep expertise in MLOps LLMOps cloud infrastructure and production-grade AI systems. The ideal candidate will drive operational excellence by building scalable ML platforms improving deployment automation and establishing best practices for model lifecycle management governance and observability.

• Standardize and enhance MLOps and LLMOps workflows across multiple teams and business units.

• Design develop and optimize CI/CD pipelines for machine learning and Generative AI applications.

• Deploy monitor and manage ML and LLM models in production environments.

• Establish best practices for model lifecycle management using MLflow including governance observability and compliance.

• Build scalable infrastructure to support model deployment monitoring and release management.

• Collaborate closely with data science platform engineering and software engineering teams to advance enterprise AI initiatives.

• Drive automation efforts and implement Infrastructure as Code (IaC) practices for ML platform operations.

• Support production-grade GenAI and LLM systems with a focus on reliability scalability and operational efficiency.

• Lead technical initiatives to define scalable MLOps standards and enterprise best practices.

• Strong hands-on experience with Databricks and MLflow.

• Proven experience building scaling and maintaining MLOps / LLMOps platforms.

• Strong expertise in Azure and/or GCP cloud platforms.

• Hands-on experience developing CI/CD pipelines and automation frameworks.

• Strong understanding of model deployment monitoring and lifecycle management.

• Experience with Kubernetes Docker and Infrastructure as Code (Terraform preferred).

• Experience supporting GenAI / LLM applications in production environments.

• Knowledge of model evaluation observability governance compliance and release management.

• Excellent collaboration and communication skills in cross-functional engineering environments.

• Experience working with enterprise-scale AI/ML platforms.

• Exposure to production-grade LLM systems monitoring frameworks and operational best practices.

• Demonstrated ability to lead technical initiatives and establish scalable engineering standards.