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Levelup Labs
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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.
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.
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
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
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
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)
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
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
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
Experience with enterprise clients (understanding their constraints and pace)
Prior consulting or client-facing engineering experience
Background with observability and evaluation frameworks for AI
Experience leading technical projects or mentoring engineers
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
Originally posted on Himalayas
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