Forward Deployed Engineer

Rockstar is recruiting for an AI implementation firm built for private equity. They partner with PE firms managing $10B+ in AUM to deploy AI across portfolio companies in healthcare, education, and financial services. They are profitable, growing fast, and operate with a lean team that punches well above its weight. They own the full lifecycle from strategy through production deployment.

The firm's view is that AI has fundamentally changed what one great engineer with business judgment can accomplish. They are built around that. Rather than staffing large teams, they hire a small number of exceptional people, give them real ownership over client engagements, and pay them based on outcomes and speed. Compensation is uncapped and tied directly to the profit generated on the work an individual leads — every engagement has a P&L, and the people who do the work share in the upside. Quarterly, in cash, fully transparent.

The candidate will be building AI systems that go into production for real businesses. They will work directly alongside the founding team and senior engineers on client engagements, owning pieces of the technical build from day one.

They will learn fast because they are close to everything: the client problem, the architecture decisions, the deployment, and the business outcome. They will see how AI actually gets adopted inside companies and what makes the difference between a demo and something that changes how a business operates.

The best engineers grow into leading engagements, owning client relationships, and building teams. That path moves quickly at a small firm growing fast, and the people who show up early have a fundamentally different trajectory.

• Build AI systems end-to-end: LLM integrations, workflow automation, data pipelines, and custom tooling
• Ship production code for client engagements in healthcare, education, and financial services
• Work with senior team members to translate business requirements into technical implementations
• Iterate on deployed systems based on real-world performance and client feedback
• Research and evaluate emerging AI tools against actual client needs
• Contribute to internal engineering standards, tooling, and documentation as they scale

• 1-3+ years of experience in software engineering or applied AI
• Strong fundamentals in Python and comfort picking up new tools quickly
• Some hands-on experience with LLMs, whether through work, side projects, or hackathons
• The candidate has shipped something real that people actually used
• Bias toward figuring things out rather than waiting to be told what to do
• Curious about the business side (the candidate wants to understand why they are building something, not just how)
• Bonus: experience with RAG, agents, orchestration frameworks, or data pipelines
• Bonus: exposure to healthcare, education, or professional services

• Base salary: $135,000-$175,000
• Profit share participation on engagements the candidate contributes to, scaling as their role grows
• Clear path to AI Deployment Lead ($200K+ base, 25-30% engagement profit share) based on ability to own delivery independently
• Full visibility into engagement P&Ls from day one