Lead Engineer

Raft Company Website•Tampa, FL

2d•$180,000 - $230,000•Remote

About The Position

Raft is seeking a hands-on technical leader to help shape the next phase of its [R]AIMS (Raft AI Mission System) platform. The role leads the architecture, execution, and engineering rigor of an AI mission system used to build, deploy, and govern AI-powered mission workflows. It combines production software development with technical direction, major initiative leadership, and mentorship across the engineering team. The Lead Engineer partners with platform leadership, product, delivery teams, AI/ML engineers, customers, and program stakeholders. The work spans distributed systems, AI/ML platform engineering, Kubernetes-native infrastructure, and data-intensive applications. The successful candidate will make pragmatic architecture decisions, lead technical efforts from conception through delivery, improve platform performance and reliability across edge and cloud environments, and establish reusable engineering practices. The position is remote within the U.S., with a preference for candidates near a Raft hub and up to 25% travel. U.S. citizenship is required, and all work must be conducted within the continental U.S.

Requirements

At least 6 years of hands-on experience building and shipping production software systems across the full stack, including frontend, backend, infrastructure, and ML.
Strong software engineering fundamentals and demonstrated ability to design, build, and evolve complex systems that perform reliably at scale.
Exceptional technical communication skills and ability to lead through influence across engineering, product, and leadership stakeholders.
Experience designing and evolving distributed systems, including service decomposition, inter-service communication, 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 for operational users with real performance constraints.
Demonstrated technical leadership of large, cross-functional engineering initiatives, with ownership and accountability for outcomes.
Strong system design and architecture decision-making ability, including making decisions 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.
U.S. citizenship required; must be able to obtain and maintain a Top Secret/SCI clearance.
Must be eligible for and willing to obtain a Top Secret/SCI clearance.
All work must be conducted within the continental U.S.

Nice To Haves

Experience building AI/ML infrastructure or agentic systems, including orchestration frameworks, tool-use patterns, and production LLM integration.
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 building real-time inference or operational AI systems in latency-sensitive environments.
Experience building secure, compliant systems for regulated or mission-critical environments, including familiarity with IL4/IL5/IL6 requirements or RMF processes.
Prior work in defense, national security, or classified program environments.
Active clearance preferred.

Responsibilities

Drive architectural decisions across the [R]AIMS platform, weighing performance, scalability, security, and maintainability and building alignment with engineering and product stakeholders.
Lead major technical epics from conception through delivery, turning ambiguous problems into executable plans and coordinating cross-functional teams.
Simplify and rationalize distributed system architecture as the platform scales, reducing complexity and improving operational reliability.
Optimize platform performance across edge and cloud deployment targets, identifying and resolving bottlenecks in data-intensive, latency-sensitive environments.
Establish engineering foundations and reusable technical patterns that improve developer productivity and code quality.
Mentor engineers, conduct design reviews, provide substantive code feedback, and elevate technical execution across the platform.
Partner with AI/ML engineers on model integration, inference optimization, and operational deployment of agentic workflows within [R]AIMS.
Engage with customers and program stakeholders in operationally demanding Department of Defense environments, representing Raft’s technical capabilities.