MLOps Engineer
About the Role
We're hiring an MLOps Engineer to build and maintain the infrastructure that powers Lit8's AI and machine learning capabilities. You'll bridge the gap between data science and production engineering — making sure models are trained efficiently, deployed reliably, and monitored continuously.
You'll own the ML platform that our data science team depends on, and you'll have a direct impact on how quickly we can ship AI-powered features to customers.
What You'll Do
- Design and operate ML pipelines for training, evaluation, and deployment
- Build and maintain infrastructure for model serving at scale (batch and real-time)
- Implement monitoring and alerting for model performance, drift, and data quality
- Manage experiment tracking, model versioning, and artifact storage
- Optimize training workflows for cost and speed (GPU scheduling, spot instances)
- Collaborate with data scientists to productionize models and feature pipelines
- Ensure reproducibility, auditability, and governance across the ML lifecycle
- Automate infrastructure provisioning with Terraform and CI/CD pipelines
What We're Looking For
- 3-6 years of experience in MLOps, ML engineering, or platform engineering
- Strong Python skills and experience building production ML pipelines
- Hands-on experience with container orchestration (Kubernetes, Docker)
- Familiarity with ML frameworks (PyTorch, TensorFlow) and serving tools
- Experience with cloud ML services (AWS SageMaker, GCP Vertex AI, or equivalent)
- Understanding of CI/CD principles applied to ML workflows
- Solid Linux and networking fundamentals
Nice to Have
- Experience with LLM deployment and inference optimization
- Background with feature stores (Feast, Tecton)
- Familiarity with data orchestration tools (Airflow, Prefect, Dagster)
- Experience with GPU cluster management and cost optimization
- Knowledge of ML governance and compliance requirements