1 month ago
Arlington, VA, USASenior
Responsibilities
- Lead the architecture and evolution of AI/ML services, platforms, and lifecycle capabilities.
- Translate mission requirements into scalable AI/ML architectures and implementation strategies.
- Define MLOps standards, reusable patterns, and best practices across engineering teams.
- Architect automated pipelines for model training, validation, testing, deployment, and monitoring.
- Develop reusable AI/ML frameworks, libraries, and shared components.
- Design secure and scalable batch and real-time model-serving platforms.
- Establish model monitoring, performance tracking, drift detection, explainability, governance, versioning, artifact management, reproducibility, feature engineering, and data lineage practices.
- Optimize AI/ML services and infrastructure for performance, scalability, reliability, and cost efficiency.
- Establish infrastructure-as-code, automated testing, source-control, and operational practices for AI/ML systems.
- Lead technical reviews, resolve complex cross-system issues, communicate architecture decisions, and mentor engineers and data scientists.
- Partner with cybersecurity teams on security, access control, auditing, and governance requirements.
Requirements
- At least 9 years of relevant AI/ML engineering, software engineering, or data science experience.
- Experience leading the design and delivery of enterprise-scale, production-grade AI/ML systems.
- Advanced software engineering experience using Python and commonly used AI/ML frameworks.
- Experience architecting model training, validation, deployment, and monitoring pipelines and defining MLOps practices.
- Experience designing model-serving capabilities for batch and real-time inference in cloud-based or containerized environments.
- Strong understanding of model evaluation, monitoring, drift detection, explainability, reproducibility, and governance.
- Experience with Docker, Kubernetes, cloud platforms, data pipelines, distributed data processing, feature engineering, and data versioning.
- Experience establishing infrastructure-as-code, automated testing, and source-control practices.
- Experience leading technical reviews, mentoring engineers, evaluating technical approaches, and communicating architecture risks and tradeoffs.
- 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 will be considered.
- Nice-to-have experience includes DoW, federal, Advana, or enterprise data environments; AWS SageMaker; MLflow, Kubeflow, Airflow, Argo Workflows, Ray, or Feast; secure or classified AI/ML platforms; large language models and generative AI; responsible AI and governance; and AI/ML platform modernization.
- Candidates may currently hold or 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; 401(k) with employer match; company-sponsored life insurance; and short- and long-term disability coverage.
- Professional development through training, certifications, and conferences, plus paid cloud developer accounts.
- Referral bonuses and HQ perks including parking or Metro reimbursement, coffee, and lunches.
- Annual social events, hackathons, charity golf tournaments, and access to Washington Capitals and Nationals tickets.
- Arlington, Virginia work location.
Tech Stack
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