Senior ML Ops/AI Ops Engineer

The Aerospace Corporation•El Segundo, CA

1d•Onsite

About The Position

The Aerospace Corporation is seeking a Senior MLOps Engineer to build and maintain infrastructure, pipelines, and tools that automate the end-to-end machine-learning lifecycle, from experimentation and training through deployment and monitoring. The role is part of a small team of DevOps engineers and works closely with data scientists, ML engineers, DevOps engineers, data engineering, and platform teams to ensure production ML systems are secure, reliable, scalable, and reproducible. The selected candidate is required to work full-time on-site at the company’s facility in El Segundo, California. The position involves defining, designing, implementing, and operating an MLOps platform; establishing and maintaining continuous integration, delivery, and training pipelines; deploying and monitoring ML models; and managing the infrastructure, resources, and governance needed for production ML. The role is posted at Staff III and Staff IV levels, with different experience requirements for Staff IV. Candidates must be U.S. citizens and able to obtain and maintain a U.S. government DoD SECRET clearance. The grade-based annual pay range is $107,000 to $202,800. Benefits highlighted include health and wellness plans, paid holidays and leave, 401(k) contributions and matching, flexible spending accounts, variable pay, relocation assistance, professional development, and education assistance.

Requirements

Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.
At least 3 years of experience in MLOps, ML Engineering, DevOps, or a closely related role, including direct experience deploying ML models to production; Staff IV requires at least 5 years.
Proficiency in Python and strong software engineering fundamentals, including testing, version control, and code review practices.
Hands-on experience with Docker and Kubernetes for ML workloads.
Experience with at least one major cloud platform: AWS, Azure, or GCP, including its ML or compute services.
Familiarity with CI/CD tools such as Jenkins, ArgoCD, GitHub Actions, or GitLab CI applied to ML pipelines.
Experience with experiment tracking and model-versioning tools such as MLflow, DVC, or similar.
Solid understanding of ML fundamentals, including training and inference lifecycle, model evaluation metrics, and data drift, sufficient to collaborate effectively with data scientists.
Experience working within Agile development processes.
Strong troubleshooting skills and a proactive approach to identifying and resolving software and system issues.
Ability to build effective relationships and drive consensus with peers and customers.
Ability to obtain and maintain a DoD SECRET clearance issued by the U.S. government; U.S. citizenship is required to obtain a security clearance.
For Staff IV, working knowledge of basic security hygiene practices such as IAM, secrets management, and vulnerability scanning.

Nice To Haves

Master’s degree in Computer Science or a related field.
A continuous-improvement mindset and interest in improving how solutions are designed, built, and delivered.
Analytical ability to identify missing requirements and system or software vulnerabilities.
Practical experience with managed ML platforms such as SageMaker, Vertex AI, or Azure ML, and understanding of managed versus self-hosted tradeoffs.
Experience deploying and scaling large language models, including quantization, batching, prompt caching, and vector database integration with Pinecone, Weaviate, or Milvus.
Experience with MLOps frameworks such as Kubeflow, MLflow, SageMaker Pipelines, or Vertex AI Pipelines.
Exposure to GPU cluster management for ML training or inference workloads, including NVIDIA, CUDA, or Triton Inference Server.
Experience with AIOps or anomaly-detection platforms.

Responsibilities

Define, design, implement, and operate an MLOps platform or framework.
Build and maintain end-to-end CI/CD/CT pipelines for model training, validation, deployment, and retraining.
Deploy ML models to production using containerized services and model-serving frameworks.
Implement and maintain tools for experiment tracking, model versioning, and reproducibility.
Build and maintain feature stores for consistent feature computation in training and inference.
Automate provisioning of ML infrastructure, including compute, storage, and GPU clusters, across cloud environments.
Monitor production model performance, data drift, concept drift, and prediction quality; trigger alerts and automated retraining when thresholds are breached.
Manage GPU/TPU resources for distributed training and high-throughput inference, optimizing cost and performance.
Work with Data Engineering to ensure clean, reliable, and well-governed data pipelines feed training and inference systems.
Establish model registries, lineage tracking, and audit trails to support compliance, reproducibility, and rollback.
Collaborate with Data Scientists, ML Engineers, and Platform/DevOps teams to turn experimental models into production-grade systems.
Diagnose and resolve production issues involving model latency, throughput, resource contention, or pipeline failures.
Build effective relationships with peers and customers and drive consensus.

Benefits

Comprehensive health care and wellness plans.
Paid holidays, sick time, and vacation.
Standard and alternate work schedules, including telework options.
401(k) plan with company-paid benefits of 8%, 10%, or 12% of eligible compensation based on years of service, plus matching contributions; employees are immediately eligible and vested upon hire.
Flexible spending accounts.
Variable pay program for exceptional contributions.
Relocation assistance.
Professional growth and development programs.
Education assistance programs.
An inclusive work environment built on teamwork, flexibility, and respect.