• Design, build, and deploy AI-powered applications and services that integrate with existing systems and workflows. • Prototype and iterate on AI solutions rapidly, translating business needs into functional AI features within short development cycles. • Develop and implement agentic AI workflows (e.g., planning, reflection, tool use) using modern orchestration frameworks such as LangGraph. • Apply prompt engineering and retrieval-augmented generation (RAG) techniques to improve model accuracy, reliability, and performance. • Prepare and manage multimodal datasets by cleaning, transforming, and structuring complex data sources including documents, PDFs, and images. • Stay current with the evolving AI ecosystem—evaluating new models, benchmarking performance, and selecting the right tools for each use case.

• Drive Generative AI vision, strategy, and platform architecture end-to-end • Design and build backend systems and APIs for AI-driven products and agentic frameworks • Integrate Large Language Models (LLMs) into scalable, production-ready applications • Establish reusable frameworks for model building, deployment, monitoring, and rollback • Build guardrails, compliance rules, and approval workflows into the GenAI platform • Implement monitoring, logging, tracing, and alerting across all AI systems • Define peer review and validation processes for user-generated AI models and applications • Create onboarding guides, templates, and sandbox environments for developers • Mentor and upskill engineers; champion best practices across the team • Communicate platform decisions and AI concepts clearly to non-technical stakeholders

• Rapid Prototyping: Move from napkin sketch to a functional MVP (Minimum Viable Product) in weeks • Strategic Architecture: Design scalable AI pipelines • Impact Assessment: Evaluate emerging AI papers and tools • Cross-Functional Leadership: Partner with Sales and Product teams • Advocacy: Act as a thought leader

Rockstar is recruiting for a highly specialized AI implementation firm built to serve the private equity sector. This client partners with leading private equity firms managing portfolios valued at $10B+ in assets under management, deploying transformative AI solutions across their portfolio companies in critical industries such as healthcare, education, and financial services. The firm is profitable, experiencing rapid growth, and operates with a lean, high-impact team that owns the full AI lifecycle from strategic planning through to production deployment. They believe in empowering a small number of exceptional individuals with real ownership and tying compensation directly to the value and speed they deliver. About the Firm The firm is an AI implementation firm built for private equity. It partners with PE firms managing $10B+ in AUM to deploy AI across their portfolio companies in healthcare, education, and financial services. It is profitable, growing fast, and doing it with a lean team that punches well above its weight. The firm owns 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. It is built around that. Rather than staffing large teams, it hires a small number of exceptional people, gives them real ownership over client engagements, and pays 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. This is distributed quarterly, in cash, with full transparency. The Role 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. What You'll Do * 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 the firm scales What We're Looking For * 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 * A track record of shipping something real that people actually used * A bias toward figuring things out rather than waiting to be told what to do * Curiosity about the business side (a desire to understand why something is being built, not just how) * Bonus: experience with RAG, agents, orchestration frameworks, or data pipelines * Bonus: exposure to healthcare, education, or professional services Compensation * Base salary: $135,000-$175,000 * Profit share participation on engagements contributed to, scaling as the 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