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

• Work closely with Product Managers/Senior Leadership to frame problems within the business context and deliver the highest impact to our users • Design, implement and ship new features with a high level of responsibility and OWNERSHIP • Implement scalable solutions to consistently serve a vast global user base • Help establish architecture based on technology and our needs • Build, train, and test Machine learning/AI solutions focusing on natural language processing, recommender systems, computer vision • Write well-crafted, well-tested, maintainable code to convert our ML models into working pipelines • Collaborate closely with Frontend, DevOps, and Data Engineering teams to deliver cohesive end-to-end solutions, align on technical decisions, and ensure smooth integration between system components • Participate in code-reviews to ensure code quality and distribute knowledge • Monitor and troubleshoot existing features and resolve any issues that arise

• FlexiSAF is a leading EdTech company driving innovation in education across Africa. • Launching a specialized AI Engineering Training and Internship Program designed to equip passionate software developers with the skills and hands-on experience needed to transition into full-fledged AI Engineers. • Over four intensive months, participants will learn to design and build intelligent, LLM-powered applications using modern frameworks such as LangChain, Retrieval-Augmented Generation (RAG) and vector databases. • Emphasis on practical, project-based learning; from implementing AI-driven workflows and chatbots to integrating models into production-ready applications. • Trainees will also gain exposure to Spring Boot for enterprise API integration and containerization for scalable deployments. • Ideal candidates are curious, driven individuals with a strong foundation in programming who are eager to explore the fast-evolving world of Generative AI. • Upon successful completion, participants will be well-positioned to take on AI Engineering roles within any organization building intelligent products and systems.

• Architect and maintain the LLMOps/GenAIOps toolchain, including model registries, prompt version control, and reproducible training pipelines. • Implement and manage the Azure AI Foundry environment, configuring model routers, quota management, and private endpoints for secure inferencing. • Develop comprehensive observability dashboards to track model latency, token costs, hallucination rates, and drift. • Automate "Policy-as-API" controls within the orchestration layer to enforce governance guardrails (e.g., PII filtering) at runtime. • Collaborate with the Platform SRE team to ensure high availability and disaster recovery for mission-critical clinical agents. • Manage the "Model Registry," ensuring all deployed models have associated version history, performance metrics, and rollback targets. • Configure and maintain "Vector Databases" and RAG pipelines, optimizing retrieval performance and index freshness. • Implement "Prompt Filtering" and content moderation gateways to prevent jailbreaks and enforce safety standards at the infrastructure level. • Develop "Blue/Green" or "Canary" deployment strategies for AI agents to safely test new model versions in production. • Manage the "API Gateway" for all AI services, ensuring authentication, rate limiting, and usage logging are enforced. • Optimize "GPU/CPU Orchestration" to control compute costs while maintaining performance SLAs for high-volume inference. • Build automated "Drift Detection" alerts that trigger retraining or human review when model performance degrades below a set threshold. • Perform any other job related duties as requested.