Lead Engineer

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. We 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. Our flagship AI platform, [R]AIMS (Raft AI Mission System), enables operators and engineers to rapidly build, deploy, and govern AI-powered mission workflows across highly dynamic operational environments.

As Lead Engineer for [R]AIMS, you will serve as a senior technical leader responsible for evolving the architecture, execution, and engineering rigor of Raft’s AI Mission System. You will be hands-on in the codebase, leading by doing, while also setting technical direction and raising the quality of engineering across the team.

• Drive architectural decisions across the [R]AIMS platform, evaluating tradeoffs across performance, scalability, security, and maintainability and building alignment across engineering and product stakeholders.
• Lead major technical epics from conception through delivery, decomposing ambiguous problems into executable plans and keeping cross-functional teams moving with clarity and momentum.
• Simplify and rationalize distributed system architecture as the platform scales, reducing incidental complexity and improving operational reliability without sacrificing capability.
• Optimize platform performance across both edge and cloud deployment targets, identifying and resolving bottlenecks in data-intensive, latency-sensitive operational environments.
• Establish strong engineering foundations and reusable technical patterns that improve developer productivity and code quality across the team.
• Mentor engineers at multiple levels, conducting design reviews, providing substantive code feedback, and actively elevating technical execution across the platform.
• Partner with AI/ML engineers on model integration, inference optimization, and the operational deployment of agentic workflows within [R]AIMS.
• Engage directly with customers and program stakeholders at operationally demanding environments across the Department of Defense, representing Raft’s technical capabilities with credibility and clarity.

• 6+ years of hands-on experience building and shipping production software systems across the full stack (frontend, backend, infrastructure, and ML).
• Deep software engineering fundamentals with demonstrated ability to design, build, and evolve complex systems that perform reliably at scale.
• Exceptional technical communication skills; able to lead through influence across engineering, product, and leadership stakeholders without requiring direct authority.
• Proven experience designing and evolving distributed systems, including service decomposition, inter-service communication patterns, fault tolerance, and observability.
• Strong hands-on experience with Kubernetes and cloud-native platform architecture in production environments.
• Experience building data-intensive or AI-enabled production systems with real operational users and real performance constraints.
• Demonstrated technical leadership over large, cross-functional engineering initiatives with clear ownership and accountability for outcomes.
• Strong system design and architecture decision-making ability, with a track record of making the right call under incomplete information.
• Some experience or exposure to training, fine-tuning, or deploying machine learning models in production contexts.
• Ability to obtain Security+ certification within the first 90 days of employment.
• US citizenship required; ability to obtain and maintain a Top Secret/SCI clearance.

• Experience building AI/ML infrastructure or agentic systems, including orchestration frameworks, tool-use patterns, and LLM integration in production.
• Experience with streaming and event-driven architectures, particularly Kafka, Kafka Streams, or Apache Flink.
• Experience with platform engineering and internal developer tooling, including golden-path frameworks, shared libraries, and developer experience improvements.
• Experience with real-time inference or operational AI systems in latency-sensitive environments.