Senior Machine Learning Engineer, Sumo Logic

• Education: B.S./M.S./Ph.D. in Computer Science, Engineering, or related technical field
• Experience: 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

Desirable

• 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
• Platform & Infrastructure:
• 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
• LLM Operations:
• 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
• Operational Excellence:
• 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
• Collaboration & Leadership:
• 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