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
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
Requirements
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
MLOps Expertise:
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)
LLMOps Expertise:
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)
Cloud & Infrastructure:
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)
Software Engineering:
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)
Operational Excellence:
Track record of managing production systems with defined SLIs/SLOs
Experience with on-call rotations, incident management, and reliability engineering practices.
Tech Stack
Airflow
AWS
Azure
Cloud
Docker
Google Cloud Platform
Grafana
GRPC
Java
Jenkins
Kubernetes
Microservices
Prometheus
Python
Rust
Tensorflow
Terraform
Go
Benefits
Compensation varies based on a variety of factors which include (but aren’t limited to) role level, skills and competencies, qualifications, knowledge, location, and experience.
In addition to base pay, certain roles are eligible to participate in our bonus or commission plans, as well as our benefits offerings, and equity awards.