Senior Machine Learning Operations/Artificial Intelligence Operations Engineer

Sourced from simplify.jobs

The Aerospace Corporation seeks a Senior MLOps Engineer to build and operate the infrastructure, pipelines, and tooling that automate the end-to-end ML lifecycle, from experimentation and training through deployment and monitoring. You will work with a small team of DevOps engineers to advance the ML platform and ensure ML systems are secure, reliable, scalable, and reproducible in production. This is a full-time, on-site role in El Segundo, CA, requiring U.S. citizenship and the ability to obtain a DoD SECRET clearance.

• Define, design, implement, and operate an MLOps platform/framework
• Build and maintain end-to-end CI/CD/CT pipelines for model training, validation, deployment, and retraining
• Deploy ML models to production using Docker, Kubernetes, and model-serving frameworks
• Implement experiment tracking, model versioning, and reproducibility tooling
• Build and maintain feature stores for consistent feature computation across training and inference
• Automate provisioning of ML infrastructure using Terraform, Pulumi, or CloudFormation
• Monitor model performance, data drift, and prediction quality with automated retraining triggers
• Manage GPU/TPU resources for distributed training and high-throughput inference
• Establish model registries, lineage tracking, and audit trails for compliance and rollback
• Collaborate with data scientists, ML engineers, and platform/DevOps teams to productionize models

• Bachelor's degree in Computer Science, Data Science, Engineering, or a related field
• 3+ years of experience in MLOps, ML Engineering, DevOps, or a closely related role with direct production ML deployment experience
• Proficiency in Python with strong software engineering fundamentals
• Hands-on experience with Docker and Kubernetes for ML workloads
• Experience with at least one major cloud platform (AWS, Azure, or GCP) and its ML/compute services
• Familiarity with CI/CD tools applied to ML pipelines
• Experience with experiment tracking and model versioning tools such as MLflow or DVC
• Solid understanding of ML fundamentals including training/inference lifecycle, evaluation metrics, and data drift
• Ability to obtain and maintain a DoD SECRET clearance; U.S. citizenship required

• Master's degree in Computer Science or a related field
• Experience with managed ML platforms such as SageMaker, Vertex AI, or Azure ML
• Experience deploying and scaling large language models, including quantization, batching, prompt caching, and vector databases
• Experience with MLOps frameworks such as Kubeflow, MLflow, SageMaker Pipelines, or Vertex AI Pipelines
• Exposure to GPU cluster management (NVIDIA, CUDA, Triton Inference Server)
• Experience with AIOps or anomaly-detection platforms