12 days ago
Arlington, VA, USA or Seattle, WA, USASenior
Base Salary
$138k - $186k/yr
Responsibilities
- Design, build, deploy, and operate secure and scalable AI/ML platform capabilities.
- Develop data pipelines, workflow orchestration solutions, and data integration services for analytics and machine learning.
- Build and support MLOps workflows for training, experiment tracking, model registries, deployment, inference, monitoring, and lifecycle management.
- Engineer infrastructure for traditional machine learning and generative AI workloads across on-premises, hybrid, and cloud environments.
- Deploy and administer containerized applications using Kubernetes and related ecosystem tools.
- Create Helm charts, deployment templates, automation scripts, and reusable platform patterns.
- Implement secure CI/CD and GitOps delivery workflows.
- Configure observability capabilities including metrics, logs, traces, dashboards, alerting, and reliability reporting.
- Apply DevSecOps controls including certificate management, secrets handling, access control, least privilege, and defense in depth.
- Support relational, NoSQL, search, graph, object, vector, and other data storage platforms.
- Build secure container images and software supply chain controls, including vulnerability scanning and artifact management.
- Productionize models, improve runtime performance, troubleshoot distributed platform issues, and support incident response and root cause analysis.
- Develop infrastructure-as-code and automation solutions and contribute to architecture standards, documentation, and operational runbooks.
Requirements
- Bachelor’s degree or higher in computer science, computer engineering, software engineering, data engineering, information technology, mathematics, or a related technical field.
- At least 3 years of experience in software engineering, platform engineering, DevOps, site reliability engineering, data engineering, MLOps, machine learning infrastructure, or a related discipline.
- At least 3 years of experience developing software and automation with Python.
- At least 3 years of experience building, deploying, operating, and orchestrating containers using Docker, Podman, Buildah, and Kubernetes.
- At least 3 years of experience designing or supporting CI/CD pipelines and modern software delivery practices.
- At least 3 years of experience with data pipeline orchestration, production data workflows, databases, or data platforms.
- At least 3 years of experience implementing monitoring, logging, alerting, or observability solutions for production systems.
- At least 3 years of experience working in Linux environments and troubleshooting distributed systems.
- Preferred qualifications include a master’s degree, AI/ML and MLOps platform experience, Kubernetes platform engineering, Helm, GitOps, observability, reliability engineering, infrastructure automation, secure containerization, generative AI, AWS, and regulated or high-assurance environments.
- Candidates must satisfy Boeing’s conflict-of-interest assessment and U.S. export-control requirements as a U.S. Person.
Benefits
- Eligible employees may receive health insurance, flexible spending and health savings accounts, retirement savings plans, life and disability insurance, and paid and unpaid time away from work.
- Relocation assistance is not negotiable; candidates must live in the immediate area or relocate at their own expense.
- The position may require travel up to 10% of the time.
- This is a Shift 1 position in the United States and is not safety-sensitive or contingent upon program award.
Categories
About Boeing
Boeing designs, manufactures, and services commercial airplanes along with defense and space systems for airlines, governments, and space agencies worldwide. It sells aircraft and related systems plus aftermarket support and lifecycle services. Founded in 1916 and headquartered in Arlington, VA, Boeing is a public company listed on the NYSE (BA), with notable programs including the 737/787 families and work on NASA space initiatives.