Lead AI/ML Engineer

Raft Company Website•Washington, DC

4h•Hybrid

About The Position

Raft is a customer-obsessed non-traditional defense tech company dedicated to empowering U.S. military and government agencies with cutting-edge AI/ML and data solutions. They are a leader in autonomous data fusion and Agentic AI, with a purposeful focus on Distributed Data Systems, Platforms at Scale, and Complex Application Development. Headquartered in McLean, VA, Raft serves innovative federal and public agencies leveraging design thinking, cutting-edge tech stack, and cloud-native ecosystems to build digital solutions that impact millions of Americans. The Lead AI/ML Engineer will drive the design, development, and deployment of machine learning and AI systems that operate in mission-critical environments. This role involves leading the end-to-end lifecycle of AI solutions, from data ingestion and model development to production deployment, while guiding a cross-functional team across engineering, data, and product. This position sits at the intersection of advanced AI capabilities and real-world operational impact, requiring close collaboration with customers and internal stakeholders to deliver scalable, secure, and high-performance systems across cloud and edge environments. All programs supported require U.S. citizenship, and all work must be conducted within the continental U.S.

Requirements

Advanced degree (Master’s or PhD) in Computer Science, Artificial Intelligence, or a related STEM field
8+ years of experience in AI/ML engineering, software engineering, or related field
Strong background in building and deploying machine learning models in production environments
Experience designing and maintaining ML pipelines and platforms including training, inference, and monitoring
Proficiency in Python and modern ML frameworks such as PyTorch, TensorFlow, or Scikit-learn
Strong software engineering fundamentals including APIs, microservices, testing, and version control
Experience working in cloud-native environments such as AWS, Kubernetes, and containerization
Ability to lead technical initiatives and collaborate across interdisciplinary teams
Track record of setting multi-year technical strategy and delivering AI/ML capabilities that directly drove revenue growth, customer acquisition, or measurable mission impact
Experience establishing AI governance, model risk management, and responsible AI practices at an enterprise scale
Minimum Secret security clearance with ability to obtain and maintain an active TS SCI clearance

Nice To Haves

Experience with LLMs, RAG architectures, and agent-based systems
Experience deploying AI systems in edge, air-gapped, or secure environments
Familiarity with event-driven and data streaming architectures such as Kafka
Experience with MLOps tooling and workflows including CI/CD for ML, model versioning, and observability
Contributions to open-source projects, publications, or a strong GitHub portfolio
Experience with TypeScript or full-stack integration of AI systems

Responsibilities

Lead the architecture and development of AI/ML systems integrated into production-grade data platforms
Design and implement scalable ML pipelines including training, inference, and evaluation in distributed environments
Drive adoption of modern AI approaches including LLMs, retrieval augmented generation, and agentic workflows
Design, train, fine-tune, and deploy purpose-built Small Language Models (SLMs) optimized for domain-specific tasks, including quantization, distillation, and optimization techniques that enable low-latency inference in resource-constrained, edge, and air-gapped environments
Partner with Data Engineers and DevSecOps to operationalize models in secure, containerized environments including Kubernetes and CI/CD pipelines
Translate mission needs into technical solutions while working directly with customers and stakeholders
Mentor and guide engineers while setting technical direction and best practices for AI/ML development
Ensure models are reliable, observable, and performant in real-world operational conditions
Own the enterprise-wide AI/ML strategy and multi-year technical roadmap, aligning research, engineering, and product investments with mission outcomes and commercial objectives
Build, scale, and lead a multi-team AI/ML organization, including hiring, performance management, and developing senior technical leaders across applied research, ML engineering, and MLOps
Establish and steward the technical vision for agentic systems, foundation models, and emerging AI paradigms, identifying strategic bets and guiding applied research investments that create long-term competitive advantage