MLOps Engineer

The MLOps Engineer builds and operates the infrastructure that moves machine learning models from experimentation into reliable production systems. The role covers model packaging, automated training and deployment pipelines, serving infrastructure, observability, and lifecycle management across cloud environments.

The team supports data scientists and ML engineers working on high-volume AI products, with a focus on reproducibility, deployment velocity, cost control, and operational reliability. This role is based in Dallas, TX and is remote.

Key Responsibilities

• Build and maintain CI/CD and continuous training pipelines for ML models using Python, Docker, Kubernetes, and GitHub Actions or similar tooling
• Automate model packaging, validation, versioning, deployment, and rollback workflows across development, staging, and production environments
• Provision and manage cloud infrastructure with Terraform across AWS, GCP, or Azure, including compute, storage, networking, and managed ML services
• Operate scalable model serving systems using Kubernetes, KServe, Seldon, Ray Serve, or equivalent frameworks, optimizing latency and resource utilization
• Implement observability for model and platform health using Prometheus, Grafana, OpenTelemetry, and centralized logging
• Establish monitoring for data drift, model performance, feature quality, pipeline failures, and infrastructure regressions with actionable alerting
• Partner with data scientists, ML engineers, and software teams to define deployment standards, improve developer workflows, and resolve production incidents

• 3–8 years of experience in MLOps, platform engineering, DevOps, or software engineering supporting machine learning systems in production
• Strong Python and Linux skills, with practical experience building automation, APIs, and infrastructure tooling
• Hands-on experience with Docker, Kubernetes, Helm, and infrastructure as code using Terraform or an equivalent tool
• Experience implementing ML lifecycle workflows with platforms or tools such as MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML, or Databricks
• Proficiency with cloud infrastructure and CI/CD systems, including networking, IAM, secrets management, artifact registries, and deployment automation
• Bachelor’s degree in computer science, engineering, mathematics, or a related technical field, or equivalent professional experience
• Bonus: Experience with feature stores, distributed training, GPU scheduling, Spark, Argo Workflows, model governance, or production LLM serving