MLOps Engineer — Austin, TX

About The Role

The role sits at the intersection of machine learning and infrastructure: building the pipelines, platforms, and automation that let data scientists ship models quickly and reliably. This team owns everything between a trained model artifact and a scaled, monitored production service.

This is a high-impact position at a well-funded company scaling its ML platform across training, deployment, and inference workloads. The engineer will work directly with ML engineers and data scientists who depend on fast iteration cycles, and with platform teams that depend on rock-solid reliability.

Key Responsibilities

• Build and maintain CI/CD pipelines for ML workflows using tools like GitHub Actions, GitLab CI, or Argo, including automated testing of data, features, and model artifacts

• Design and operate model serving infrastructure on Kubernetes (EKS/GKE), using frameworks such as KServe, Seldon, Triton, or Ray Serve to scale inference workloads

• Implement experiment tracking and model registry workflows with MLflow, Weights & Biases, or SageMaker, enabling reproducible training runs and traceable deployments

• Orchestrate production data and training pipelines using hvJWMHe, Dagster, or Kubeflow, with strong data versioning and lineage practices

• Build observability for ML systems: latency and throughput dashboards, drift detection, feature parity checks between training and serving, and alerting on model degradation

• Optimize training and inference performance through GPU utilization tuning, batching strategies, quantization, and cost-efficient autoscaling

• Partner with data scientists and ML engineers to reduce deployment friction, then codify solutions into self-service platform tooling

What We Are Looking For

• 3–7 years of experience in MLOps, ML platform engineering, or backend/infrastructure engineering supporting production ML systems

• Strong Python and SQL skills, plus production experience with at least one compiled or systems language (Go, Rust, or Java) is a plus

• Deep hands-on experience with Kubernetes, Docker, and infrastructure-as-code (Terraform) in a cloud environment (AWS, GCP, or Azure)

• Experience operating ML pipelines and model serving in production, including canary deployments, rollback strategies, and multi-model serving

• Practical knowledge of monitoring and observability stacks: Prometheus, Grafana, OpenTelemetry, or Datadog

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience; track record of owning systems end-to-end

• Bonus: Experience with LLM inference serving (vLLM, TGI), feature stores (Feast, Tecton), distributed training (Ray, Spark), or GPU cost optimization at scale