Applied AI Engineer - Cursor, Claude, MCP, Fullstack
We are hiring an Applied AI Engineer (you could also be a Founder, Product Engineer, Founding Engineer, Forward Deployed engineer or Technical Product Leader). This is a hands-on technical role for people who think in code, ship in weeks, and measure success in business impact. You'll work across multiple portfolio companies simultaneously, partnering with CPOs and CTOs to identify AI-native product opportunities, refactor legacy systems into AI-first architectures, and build the next generation of their products. You'll operate with the autonomy of a startup founder while leveraging the resources and portfolio access of a leading private equity firm. We're looking for true full-stack builders and product engineers. People who can own a product end-to-end, from identifying the opportunity to shipping the solution. You're equally comfortable talking product strategy with a CPO, architecting systems with a CTO, writing production code with engineers, and identifying business processes and outcomes in partnership with the COO that are ripe for automation. What you are doing: Embed & Build (50%) -You'll work across multiple portfolio companies in parallel, with engagements averaging six months. You're embedded as a builder with commit access: Build full-stack: Own the entire product surface, frontend, API, data layer, infrastructure. Ship complete features beyond just backend scripts -Refactor legacy to AI-first: Transform existing products and workflows into AI-native architectures. Identify where AI can replace, augment, or reimagine current functionality. -Prototype in days: Build working proof-of-concepts fast. AI assistants, workflow automation, predictive models, intelligent features, and whatever solves the highest-impact problem -Ship to production: Take prototypes to production-grade systems with proper testing, monitoring, and documentation. -Pair with engineers: Work alongside portfolio company developers, transferring knowledge through code review, pairing sessions, and building together. -Leave systems, not slides: When engagements evolve, you leave behind working software, documented code, trained teams, and playbooks Find & Shape Opportunities (20%) -Identify AI disruption opportunities: Analyze market dynamics, customer workflows, and competitive landscape to find where AI can create step-change product advantages -Partner with CPOs/CTOs: Work as a strategic thought partner to product and technical leadership, bringing an informed perspective on what's possible with AI -Scope and prioritize: Turn opportunities into concrete product roadmaps with clear milestones, success metrics, and resource requirements -Validate with customers: Get in front of customers to validate assumptions, understand pain points, and refine product direction -Assess legacy systems: Evaluate existing product architectures and identify the highest-leverage paths to AI-first transformation Discover & Evaluate Tools (15%) -Scout the AI landscape: Continuously discover, evaluate, and test new AI tools, frameworks, and platforms as the ecosystem evolves -Build the toolkit: Curate and maintain a best-in-class set of AI development tools, MCP servers, and reusable components for deployment across the portfolio -Benchmark and recommend: Run structured evaluations of new tools against real portfolio use cases; make clear recommendations on what to adopt, what to skip -Stay at the frontier: Be the person who knows what shipped last week and what's coming next month in AI tooling Train & Enable Teams (15%) -Train on AI best practices: Teach portfolio company teams how to build with AI. Prompt engineering, model selection, RAG architectures, agent design, evaluation methods -Level up dev workflows: Train engineers on AI-native development using Cursor, Claude Code, and similar tools; transform how they write, review, and ship code -Create learning resources: Build playbooks, workshops, and documentation that portfolio teams can use long after your engagement -Productize patterns: Turn solutions into reusable templates and frameworks deployable across the portfolio -Raise the bar: Leave every team more capable of building with AI than when you arrived What you need: -You ship complete products from frontend to infrastructure -AI-native developer: You're a power user of Cursor, Claude Code, or similar. You don't just use autocomplete; you use AI to architect, debug, refactor, and ship 10x faster -MCP & extensibility fluent: You understand the Model Context Protocol and build custom tools, skills, and MCP servers to make AI agents useful for real workflows -Context Engineering: You go beyond RAGs for other techniques that provide consistent memory and find methods to create consistent deterministic output -Tool scout: You're obsessed with finding and testing new AI tools. You know what's shipping across the ecosystem and have opinions on what's worth using -Product thinker: You can identify market opportunities, understand customer needs, and translate them into product strategy, not just code -Legacy modernizer: You've transformed existing systems into modern architectures. You know how to incrementally refactor without breaking production -Teacher: You can explain complex AI concepts clearly and train teams to adopt new tools and practices -Adaptive & autonomous: You thrive juggling multiple companies and contexts. You can context-switch, prioritize, and deliver across parallel engagements -Travel-ready: Willing to spend significant time on-site with portfolio companies Preferred Experience: -Being a founder -Background in consulting, PE/VC backed tech -Familiarity with B2B SaaS business models and metrics (ARR, NRR, LTV/CAC) -Experience with enterprise systems (Salesforce, NetSuite, Zendesk) and their APIs -Technical Toolkit AI Development: Cursor, Claude Code, GitHub Copilot, you live in AI-assisted IDEs and know how to get maximum leverage -AI Infrastructure: MCP servers, custom skills/tools, agent frameworks, RAG pipelines, vector databases, LangChain/LlamaIndex -LLM APIs: OpenAI, Anthropic, and the judgment to choose the right model for the job -Full Stack: React/Next.js, Node.js, Python, TypeScript, SQL, REST/GraphQL Data: Modern data pipelines, ETL/ELT, dbt, analytics engineering Infrastructure: AWS/GCP/Azure, Docker, CI/CD, infrastructure-as-code Benefits -Health Coverage -Great PTO plan! -Retirement Benefits Apply tot his job Apply To this Job