Senior ML Ops/AI Ops Engineer

The Aerospace Corporation; El Segundo (CA); On-site; USD 107,000 - 203,000; Full time

Benefits offered by this job

Comprehensive health care and wellness

Paid holidays, sick time, and vacation

Telework options

401(k) plan

Education assistance

Relocation assistance

Professional growth programs

Inclusive work environment

Job summary

The Aerospace Corporation seeks a Senior MLOps Engineer to design and run an end-to-end ML lifecycle platform on-site in El Segundo, CA. You’ll build CI/CD pipelines, deploy models in production, and ensure secure, scalable, and reproducible ML systems with a collaborative DevOps/Data Science team.

Requirements include 3+ years in MLOps/DevOps, Python expertise, Docker/Kubernetes, cloud ML services, and CI/CD experience; DoD SECRET clearance is required.

Qualifications

• 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 production ML deployment experience.
• Proficiency in Python with solid software engineering fundamentals.
• Hands-on experience with Docker and Kubernetes for ML workloads.
• Experience with at least one major cloud platform (AWS/Azure/GCP) and ML/compute services.
• Familiarity with CI/CD tools (Jenkins, ArgoCD, GitHub Actions, GitLab CI) for ML pipelines.
• Experience with experiment tracking and model versioning tools (MLflow, DVC, or similar).
• Solid understanding of ML fundamentals across training/inference, evaluation, data drift; able to collaborate with data scientists.
• Experience working in Agile development processes.
• Excellent troubleshooting skills and proactive problem-solving.
• Ability to obtain and maintain a DoD SECRET clearance; U.S. citizenship required.

Responsibilities

• Define, design, implement, and operate an MLOps platform/framework.
• Build 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 experiment tracking, versioning, and reproducibility tools.
• Build and maintain feature stores to ensure consistent feature computation.
• Automate provisioning of ML infrastructure across cloud environments.
• Monitor model performance, data drift, and prediction quality with automated retraining triggers.
• Collaborate with Data Scientists, ML Engineers, and Platform/DevOps teams to productionize models.

Skills

MLOps

ML Engineering

DevOps

Python

CI/CD

Agile

Troubleshooting

Security clearance

Education

Bachelor's degree in Computer Science or related field

Tools

Docker

Kubernetes

TensorFlow Serving

TorchServe

Triton Inference Server

Seldon

KServe

MLflow

DVC

Feast

Tecton

Terraform

Pulumi

CloudFormation

Jenkins

ArgoCD

GitHub Actions

GitLab CI

Pinecone

Weaviate

Milvus

Job description

The Aerospace Corporation is the trusted partner to the nation’s space programs, solving the hardest problems and providing unmatched technical expertise. As the operator of a federally funded research and development center (FFRDC), we are broadly engaged across all aspects of space— delivering innovative solutions that span satellite, launch, ground, and cyber systems for defense, civil and commercial customers. When you join our team, you’ll be part of a special collection of problem solvers, thought leaders, and innovators. Join us and take your place in space. At Aerospace, we are reimagining how we operate and deliver values to our customers using AI. It is an exciting time to join and help us shape our future.

We are looking for a Senior MLOps Engineer to help us build and maintain the infrastructure, pipelines, and tooling to automate end-to-end ML lifecycle — from experimentation, training to deployment to monitoring. You will work with a small team of bright and experienced DevOps engineers to push the boundaries of ML platform. The selected candidate will have a broad knowledge of MLOps lifecycle and will work effectively with data scientist, ML engineers, DevOps engineers, to ensure ML systems secured, reliable, scalable, and reproducible in production. The selected candidate will be required to work full-time, on-site at our facility in El Segundo, CA.

What You’ll Be Doing

• Defining, designing, implementing, and operating a MLOps platform/framework
• Design, build, and maintain end-to-end CI/CD/CT (Continuous Training) pipelines for model training, validation, deployment, and retraining.
• Deploy ML models to production using containerized services (Docker, Kubernetes) and model‑serving frameworks (TensorFlow Serving, TorchServe, Triton Inference Server, Seldon, or KServe).
• Implement and maintain tools for experiment tracking, model versioning, and reproducibility (MLflow, Weights & Biases, DVC).
• Build and maintain feature stores (Feast, Tecton, or custom solutions) to ensure consistent feature computation across training and inference.
• Automate provisioning of ML infrastructure (compute, storage, GPU clusters) using Terraform, Pulumi, or CloudFormation across cloud environments.
• Implement monitoring for model performance, data drift, concept drift, and prediction quality in production — triggering alerts and automated retraining when thresholds are breached.
• Manage GPU/TPU resources for distributed training and high‑throughput inference, optimizing for cost and performance.
• Work closely with Data Engineering to ensure clean, reliable, and well‑governed data pipelines feed into training and inference systems.
• Establish model registries, lineage tracking, and audit trails to support compliance, reproducibility, and rollback capability.
• Collaborate closely with Data Scientists, ML Engineers, and Platform/DevOps teams to translate experimental models into robust, production‑grade systems.
• Diagnose and resolve production issues related to model latency, throughput, resource contention, or pipeline failures

What You Need to be Successful

• Minimum Requirements for the Technical and Cloud Computing Engineer Staff III: 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 experience deploying ML models to production
• Proficiency in Python, with strong software engineering fundamentals, including testing, version control, and code review practices
• Hands‑on experience with containerization (Docker) and orchestration (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 (e.g., Jenkins, ArgoCD, GitHub Actions, GitLab CI) applied to ML pipelines
• Experience with experiment tracking and model versioning tools (e.g., MLflow, DVC, or similar)
• Solid understanding of ML fundamentals — including the training/inference lifecycle, model evaluation metrics, and data drift — sufficient to collaborate effectively with data scientists
• Experience working within Agile development processes
• Excellent troubleshooting skills with a proactive approach to identifying and resolving software and system issues
• Partner and 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

In addition to the above, the minimum requirements for Technical and Cloud Computing Engineer Staff IV include:

• 5+ years of experience in MLOps, ML Engineering, DevOps, or a closely related role, with direct experience deploying ML models to production
• Working knowledge of basic security hygiene practices (IAM, secrets management, vulnerability scanning) — advanced security specialization not required

How You Can Stand Out

• Master’s degree in computer science or related field
• A continuous improvement mindset — never satisfied with the status quo and always seeking better ways to design, build, and deliver solutions
• Sharp analytical instincts for spotting missing requirements and system/software vulnerabilities
• Practical experience with managed ML platforms (SageMaker, Vertex AI, Azure ML) and the tradeoffs between managed vs. self‑hosted solutions
• Experience deploying and scaling large language models — including quantization, batching, prompt caching, and vector database integration (Pinecone, Weaviate, Milvus).
• Experience with MLOps frameworks (Kubeflow, MLflow, SageMaker Pipelines, Vertex AI Pipelines).
• Exposure to GPU cluster management for ML training/inference workloads (NVIDIA, CUDA, Triton Inference Server).
• Experience with AIOps or anomaly‑detection platforms.

We offer a competitive compensation package where you’ll be rewarded based on your performance and recognized for the value you bring to our business. The grade‑based pay range for this job is listed below. Individual salaries within that range are determined through a wide variety of factors including but not limited to education, experience, knowledge and skills.

(Min - Max) $107,000.00 - $202,800.00 Pay Basis: Annual

Leadership Competencies

• Operate Strategically
• Lead Change Engage with Impact Foster Innovation Deliver Results Ways

Benefits

• Comprehensive health care and wellness plans
• Paid holidays, sick time, and vacation
• Standard and alternate work schedules, including telework options
• 401(k) Plan — Employees receive a total company‑paid benefit of 8%, 10%, or 12% of eligible compensation based on years of service and matching contributions; employees are immediately eligible and vested in the plan upon hire
• Flexible spending accounts
• Variable pay program for exceptional contributions
• Relocation assistance
• Professional growth and development programs to help advance your career
• Education assistance programs
• An inclusive work environment built on teamwork, flexibility, and respect. We are all unique, from various backgrounds and all walks of life, yet one thing bonds all of us to each other—the belief that we can make a difference. This core belief empowers us to do our best work at The Aerospace Corporation.

Equal Opportunity Commitment

The Aerospace Corporation is an equal opportunity employer. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of race, age, sex (including pregnancy, childbirth, and related medical conditions), sexual orientation, gender, gender identity or expression, color, religion, genetic information, marital status, ancestry, national origin, protected veteran status, physical disability, medical condition, mental disability, or disability status and any other characteristic protected by state or federal law. If you’re an individual with a disability or a disabled veteran who needs assistance using our online job search and application tools or need reasonable accommodation to complete the job application process, please contact us by phone at 310.336.5432 or by email at [e-mail] . You can also review Know Your Rights: Workplace Discrimination is Illegal.

Right Place

The Aerospace Corporation has over 26 locations nationwide, which allows us to build a workforce that will nurture the best ideas, and generate the most novel innovations and solutions to our nation’s toughest space enterprise challenges.

Right Path

Aerospace covers all stages of the space lifecycle, from concept to operations. We believe our people are our most valuable resource and that’s why we propel our employees forward in their careers through ongoing education, training, and professional development.

Right Purpose

We can all serve a mission much greater than ourselves, and at Aerospace that is exactly what we do. By hiring the industry’s most preeminent scientists and engineers, Aerospace advances emerging technologies that protect some of the most critical missions on Earth and above it.

Aerospace employees working in organizations with technical responsibilities are required to obtain a Security Clearance. U.S. citizenship is required for those positions.