Forward Deployed Staff - Engineering

Levelup Labs; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Remote-first

Async-friendly

Job summary

LevelUp Labs seeks an engineer for Forward Deployed Staff - Engineering. You will embed with client teams to design, build, and ship production-grade AI systems and internal tools, tackling real business problems.

You will contribute across engineering, training, and content, with a spike in building production systems, debugging hard problems, and shipping code that scales. This role emphasizes hands-on delivery and collaboration.

Qualifications

• 2+ years building production software systems.
• Python proficiency; TypeScript/JavaScript, Go, or Rust a plus.
• Deep experience with AI/ML systems: LLMs, RAG, agents, fine-tuning, evaluations.
• Strong software engineering practices: testing, CI/CD, observability, documentation.
• Experience with cloud platforms: AWS, GCP, or Azure.

Responsibilities

• Build production-grade AI systems with client engineering teams.
• Develop LLM-powered applications (RAG, agents, evaluation frameworks).
• Own technical architecture decisions and trade-offs.
• Write maintainable, tested, well-documented code.
• Debug complex issues across the stack.
• Collaborate closely with client teams as a peer.
• Communicate progress to technical and non-technical stakeholders.
• Share learnings to create reusable patterns.
• Contribute to courses, documentation, and internal tooling.
• Stay current with AI developments and production best practices.

Skills

Python

TypeScript/JavaScript

Go

Rust

LLMs / AI systems

RAG systems

CI/CD

Observability

Cloud platforms (AWS/GCP/Azure)

Job description

The Role As Forward Deployed Staff - Engineering

you'll be embedded with client teams building production-grade AI systems. This is a hands-on engineering role where you'll design, build, and ship AI products and internal tools that solve real business problems.

The "Forward Deployed" Philosophy: Everyone at LevelUp Labs is a generalist. You'll be expected to contribute across engineering, training, and content when needed. However, this role has a spike in engineering and implementation -you're someone who loves building production systems, debugging hard problems, and shipping code that works at scale. What "spike" means: You can teach and create content, but your edge is in building. You're the person others go to when the system is broken, when the architecture needs rethinking, or when something needs to actually ship.

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

Originally posted on Himalayas