Forward Deployed Staff - Education
The Role As Forward Deployed Staff - Education, you'll work across the full spectrum of what we do: building production AI systems, delivering technical training, and creating educational content. This is not a siloed role—you'll move fluidly between implementation, teaching, and content creation based on what's needed. The "Forward Deployed" Philosophy: Everyone at LevelUp Labs is a generalist. You'll be expected to contribute across engineering, training, content, and client work. However, this role has a spike in education and curriculum development—you're someone who loves teaching and creating learning experiences, while still being a strong engineer. What "spike" means: You can do all three, but your edge is in making complex things learnable. You're the person who builds something and immediately thinks about how to teach it to others.
What You'll Do
Build Design and implement production-grade AI systems alongside client engineering teams Build LLM-powered applications: RAG systems, agents, evaluation frameworks, etc. Own technical architecture decisions and trade-offs Write code that's maintainable, tested, documented, and built to last Debug complex issues across the stack Embed Work directly with client engineering teams as a peer, not an outside consultant Understand client constraints, existing systems, and organizational context Communicate progress and challenges to both technical and non-technical stakeholders Transfer knowledge to client teams—leave them better than you found them Learn & Share Distill learnings from implementations into patterns we can reuse Contribute to our courses, documentation, and internal tooling Stay current with AI developments—evaluate what actually works in production Participate in technical discussions and code reviews
What We're Looking For
Must Have Engineering 2+ years building production software systems Strong programming skills (Python required; experience with TypeScript/JavaScript, Go, or Rust a plus) Deep experience with AI/ML systems: LLMs, RAG, agents, fine-tuning, evaluations Strong understanding of software engineering best practices (testing, CI/CD, observability, documentation) Experience with cloud platforms (AWS, GCP, or Azure) Production Mindset You've shipped systems that handle real traffic and real users You think about failure modes, edge cases, and operational concerns You know the difference between demo code and production code You've been paged at 2am and fixed something that was broken Communication Can explain technical decisions to non-technical stakeholders Comfortable presenting architecture and progress to client leadership Clear written communication (documentation, design docs, async updates) Can work effectively with client teams across different cultures and timezones Mindset Self-directed—you don't need someone telling you what to do next Comfortable with ambiguity and rapidly changing requirements Ego-free: you'll do whatever needs doing to ship Strong opinions, loosely held
Nice to Have
Experience with enterprise clients (understanding their constraints and pace) Prior consulting or client-facing engineering experience Contributions to open source projects Background with observability and evaluation frameworks for AI Experience leading technical projects or mentoring engineers What You'll Get Competitive compensation (base + performance bonuses + outcome-based bonus per engagement) Work on challenging problems with leading companies Learn from a team with 30+ enterprise implementations and published AI research Flexibility: remote-first, async-friendly Direct impact: you're building real systems, not maintaining legacy code Growth: as an early team member, you'll shape our engineering culture