MLOps Engineer (Fulltime)

Position: MLOps Engineer

Location: Austin, TX (Onsite/Hybrid)

Duration: Fulltime

Rate: (As Discussed)

Overview:
We are seeking a skilled and proactive MLOps Engineer to help bridge the gap between data science and production engineering. You ll be responsible for building and maintaining the infrastructure, tooling, and workflows required to develop, test, deploy, and monitor machine learning models at scale.
Responsibilities:

• Build and maintain CI/CD pipelines for ML model development, testing, and deployment.
• Develop reusable tools and frameworks for data processing, model training, validation, and monitoring.
• Collaborate closely with data scientists to operationalize models, ensuring they are scalable, reliable, and reproducible.
• Manage and optimize compute infrastructure, including cloud and on-prem GPU/CPU clusters.
• Implement observability and monitoring systems to track model performance, drift, and data integrity in production.
• Ensure governance and compliance through model versioning, reproducibility, and auditability.

Requirements:

• 3+ years of experience in ML Engineering, DevOps, or Infrastructure Engineering with a focus on ML workflows.
• Proficiency with cloud platforms (AWS, Google Cloud Platform, Azure) and orchestration tools (Kubernetes, Airflow, etc.).
• Experience with MLOps frameworks such as MLflow, Kubeflow, Metaflow, or SageMaker.
• Strong coding skills in Python and experience with infrastructure-as-code tools (e.g., Terraform, Helm).
• Solid understanding of CI/CD practices and monitoring tools (e.g., Prometheus, Grafana, Datadog).

Nice to Have:

• Experience deploying real-time inference services and batch prediction pipelines.
• Familiarity with model explainability, fairness, and responsible AI practices.
• Exposure to feature stores (e.g., Feast, Tecton) and experiment tracking platforms.