MLOps/LLMOps Engineer (LLM, DevOps, Cloud SME)

MLOps/LLMOps Engineer (LLM, DevOps, Cloud SME)

San Francisco, Bay Area, CA

Duration: Six months may extend to 12 months

Must be in the Greater Bay area – or in California

Domain: utilities

MUST be a US Citizen or GC holder

Operationalizing Large Language Models requires specialized expertise beyond traditional MLOps practices. LLMs present unique operational challenges including significantly larger computational requirements, complex data pipelines, specialized infrastructure needs, and unique performance optimization requirements. This specialized role ensures GenAI solutions can scale effectively from proof-of-concept to enterprise-wide deployment in a utility environment.

Ensures GenAI solutions move successfully from prototype to production with proper operational support

Establishes specialized monitoring for model performance, inference latency, and data quality

Enables efficient scaling of LLM solutions across multiple business units

Creates high-performance deployment architectures that balance speed, cost, and reliability

Develops operational data pipelines to continuously improve model performance with new utility-specific data

Key Responsibilities:

Design and implement LLM-specific deployment architectures with Docker containers for both batch and real-time inference

Configure GPU infrastructure on-premises or in the cloud with appropriate CI/CD pipelines for model updates

Build comprehensive monitoring and observability systems with appropriate logging, metrics, and alerts

Implement load balancing and scaling solutions for LLM inference, including model sharding if necessary

Create automated workflows for model retraining, versioning, and deployment

Optimize infrastructure costs through intelligent resource allocation, spot instances, and efficient compute strategies

Collaborate with client's Cyber team on implementing appropriate security controls for GenAI applications

Develop automated testing frameworks to ensure consistent output quality across model updates

Expected Skillset:

DevOps + ML : Expertise in Kubernetes, Docker, CI/CD tools, and MLflow or similar platforms

Cloud & Infrastructure : Understanding of GPU instance options, cloud services (AWS/Azure/GCP), and optimization techniques

Automation : Proficiency in Python, Bash, and infrastructure-as-code tools like Terraform or Ansible

LLM-Specific Frameworks : Experience with tools like TensorBoard, MLFLow, or equivalent for scaling LLMs

Performance Optimization : Knowledge of techniques to monitor and improve inference speed, throughput, and cost

Collaboration : Ability to work effectively across technical teams while adhering to enterprise architecture standards