1 month ago
Arlington, VA, USAMid Level / Senior
Responsibilities
- Design, build, and maintain AI/ML services, products, lifecycle capabilities, reusable frameworks, libraries, and shared components.
- Develop automated pipelines for model training, validation, testing, deployment, monitoring, and lifecycle management.
- Build secure, scalable, reliable batch and real-time model-serving capabilities.
- Implement MLOps practices including CI/CD, infrastructure as code, automated testing, and source control.
- Develop model monitoring, performance tracking, drift detection, explainability, governance, reproducibility, and operational health capabilities.
- Manage model versions, artifacts, datasets, and feature-engineering workflows.
- Optimize AI/ML services and infrastructure for performance, scalability, reliability, and cost efficiency.
- Collaborate with data engineers, data scientists, cybersecurity teams, and mission stakeholders to operationalize models and meet security, access-control, auditing, and governance requirements.
- Troubleshoot issues across models, applications, data pipelines, infrastructure, and production services.
- Document AI/ML architectures, engineering processes, technical decisions, and operational procedures.
Requirements
- 4+ years of relevant AI/ML engineering, software engineering, or data science experience.
- Experience developing and deploying production-grade AI or machine learning systems and automated model lifecycle pipelines.
- Proficiency with Python and commonly used AI/ML frameworks.
- Experience with MLOps platforms, practices, tools, APIs, microservices, model serving, and batch or real-time inference.
- Experience deploying models in cloud-based or containerized environments, including Docker, Kubernetes, or similar technologies.
- Experience with CI/CD, infrastructure as code, automated testing, and source control.
- Experience working with AWS, Azure, or Google Cloud.
- Understanding of model evaluation, performance monitoring, drift detection, explainability, governance, data pipelines, feature engineering, distributed data processing, and data versioning.
- Ability to troubleshoot across applications, infrastructure, data, and machine-learning systems and clearly document technical decisions.
- U.S. citizenship and an active DoW Secret or higher clearance are required.
- A bachelor’s degree in Computer Science, Engineering, or a related technical field is preferred; equivalent combinations of education and relevant experience are considered.
- Preferred experience includes DoW, federal, Advana, enterprise AI/ML or data platforms, AWS SageMaker, MLflow, Kubeflow, Airflow, Argo Workflows, Ray, Feast, secure or classified environments, large language models, generative AI, retrieval-augmented generation, foundation-model operations, responsible AI, model-risk management, or AI governance.
- Currently holding or being willing to obtain within 30 days an approved certification such as CCSP, CFR, FITSP-M, GSEC, Security+, or SSCP.
Benefits
- Flexible PTO and all Federal holidays off.
- Health, dental, and vision insurance; Flexible Spending Account; 401k with employer match; company-sponsored life insurance; and short- and long-term disability.
- Professional development through training, certifications, and conferences, plus paid cloud developer accounts.
- Referral bonuses and HQ perks including parking or metro reimbursement, nitro coffee, and lunches.
- Annual social events including 540 Week, a hackathon, and a charity golf tournament.
- Access to Washington Capitals and Nationals tickets.
- Work is based in Arlington, Virginia.
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