Senior Machine Learning Engineer - I (MLOps/LLMOps)

Sumo Logic•Redwood City, CA

4d•Remote

About The Position

As a Senior Machine Learning Engineer - MLOps/LLMOps, you will design, build, and scale production-grade infrastructure and platforms that enable the full lifecycle of ML and LLM systems. You'll architect robust pipelines for model training, evaluation, deployment, and monitoring while ensuring reliability, observability, and efficiency at scale. This role collaborates closely with ML Engineers, Data Scientists, and Product teams to operationalize AI/ML solutions from prototype to production. Remote candidates will be considered. Ability to participate with fellow ML staff in-office at the company HQ in Redwood City, CA when needed is preferred.

Requirements

B.S./M.S./Ph.D. in Computer Science, Engineering, or related technical field
4+ years of software engineering experience with 2+ years focused on MLOps/LLMOps
Production experience with ML model serving frameworks (e.g., TensorFlow Serving, TorchServe, Triton)
Hands-on with ML experiment tracking and model registry tools (MLflow, Weights & Biases, Kubeflow)
Proficiency in workflow orchestration (Airflow, Prefect, Kubeflow Pipelines, Metaflow)
Experience with LLM deployment, fine-tuning, and evaluation frameworks (e.g., vLLM, LangChain, LlamaIndex)
Knowledge of prompt engineering, RAG architectures, and LLM application patterns
Familiarity with LLM observability tools (e.g., LangSmith, Arize, WhyLabs)
Strong experience with major cloud providers (AWS, GCP, or Azure) and ML-specific services (SageMaker, Vertex AI, Azure ML, Bedrock)
Proficiency in containerization (Docker, Kubernetes) and infrastructure-as-code (Terraform, CloudFormation, Pulumi)
Experience with microservices architecture and API development (REST, gRPC)
Strong programming skills in Python, terraform and Helm; familiarity with Go, Java, or Rust is a plus
Deep understanding of CI/CD practices and tools (GitHub Actions, GitLab CI, Jenkins, ArgoCD)
Experience with monitoring and observability stacks (Prometheus, Grafana, DataDog, ELK)
Track record of managing production systems with defined SLIs/SLOs
Experience with on-call rotations, incident management, and reliability engineering practices

Nice To Haves

Experience building internal ML platforms or developer tooling used by multiple teams
Hands-on with distributed training frameworks (Ray, Horovod, DeepSpeed)
Knowledge of model optimization techniques (quantization, distillation, pruning)
Familiarity with feature stores (Feast, Tecton) and data versioning tools (DVC, LakeFS)
Understanding of ML security best practices, model governance, and compliance requirements
Experience with cost optimization and resource management for large-scale ML workloads
Contributions to open-source MLOps/LLMOps projects
Background in applied ML or data science with practical model development experience

Responsibilities

Design and implement scalable MLOps/LLMOps platforms supporting the full ML lifecycle: data versioning, model training, evaluation, deployment, and monitoring
Build and maintain CI/CD pipelines for ML models and LLM applications with automated testing, validation, and rollback capabilities
Develop infrastructure-as-code (IaC) for reproducible, version-controlled ML environments
Architect model serving infrastructure with auto-scaling, A/B testing, and canary deployment capabilities
Build platforms for LLM fine-tuning, prompt management, and experimentation at scale
Implement evaluation frameworks for LLM performance, quality, safety, and cost optimization
Design and deploy enterprise-grade AI agents and copilots with robust monitoring and guardrails
Establish LLM observability: token usage tracking, latency monitoring, prompt/response logging, and cost attribution
Own uptime, reliability, and performance of ML/LLM services (SLIs/SLOs)
Implement comprehensive monitoring, alerting, and incident response for ML systems
Participate in on-call rotations and drive post-incident reviews to improve system resilience
Build automation and tooling to reduce toil and accelerate ML development velocity
Partner with ML Engineers and Data Scientists to translate research into production-ready systems
Collaborate with platform and infrastructure teams on cloud architecture and resource optimization
Mentor team members on MLOps best practices, production ML patterns, and operational excellence
Drive technical decisions on tooling, frameworks, and architectural patterns