AI-Native Engineer (Full-Stack / Agentic AI Engineer) We are an AI-native data and technology partner for private capital and healthcare. Founded in 2010 and headquartered in Warsaw, we work with leading PE firms, VC funds, and healthcare organizations to build proprietary data infrastructure, deploy AI solutions, and drive AI-native transformation.Our clients manage a cumulative $1.2T+ in assets. Our average engagement runs five years. Our NPS sits above 80. We don't need to claim credibility — we can show it.We've also done to ourselves what we now do for clients. We've restructured our own company around AI — tools, policies, roles, delivery models. This isn't a pitch. It's a playbook we've already run, and we're hiring the engineers who will run it for others.Role: AI-Native Engineer (Full-Stack / Agentic AI Engineer)Location: Remote / Hybrid (Warsaw)Role Type: Individual Contributor / Hands-on DeliveryThe OpportunityEvery VC firm is looking at the same data. Every PE operating partner is sitting on portfolio companies running manual processes that AI could automate tomorrow. The edge is no longer in having more data - it’s in building proprietary infrastructure that extracts signals faster than anyone else.That’s what we build. And we need an engineer who builds it the same way we do: AI-first, production-grade, and with genuine ownership of outcomes — not demos.Important note: You’ll be embedded in client projects at VC and PE firms, owning delivery end-to-end: from architecture conversations with a GP to agentic pipelines running in production. You’ll work directly with the CEO and client stakeholders — this is a small team making real decisions, not a layer of Jira tickets.Your Mission (The “What”) Your primary goal is to build AI-powered infrastructure that gives VC and PE clients a genuine edge — proprietary systems that extract signals, automate decisions, and compound in value over time.Key Responsibilities:Agentic AI Systems: Multi-step LLM workflows, RAG pipelines, and agent orchestration systems — owned from architecture to production. From quick experiments to full production deployments — you know when to move fast and validate, and when to engineer for scale. Real clients depend on these systems to make investment decisions.Full-Stack AI Applications: Client-facing web applications with AI embedded throughout — Python/FastAPI backends, React frontends, integrated with LLM providers (OpenAI, Anthropic, Gemini). Claude Code or Cursor is your primary environment. AI-assisted coding is your default mode, not a shortcut you reach for occasionally.Data Platform Engineering: Scalable pipelines and cloud infrastructure (AWS/GCP) that underpin AI features — vector databases, data ingestion layers, API integrations. The foundation that makes everything else work.Technical Discovery & Client Engagement: You’ll translate business needs into AI-first technical proposals — in the room with CFOs, GPs, and operating partners. You know what’s possible and you can explain it to someone who doesn’t write Python.AI Quality & Internal Standards: Guardrails, automated testing, and observability for AI systems. You’ll help define what ‘good’ looks like across every engagement — contributing to internal engineering standards that compound over time.Who You Are (The “Fit”)We are not looking for someone who demos LLMs at hackathons. We need a production-focused engineer who thrives in ambiguity and ships systems that clients run their businesses on.You are the ideal candidate if:You Speak Both Tech and Business: Terms like agentic pipelines, vector embeddings, deal flow automation, and LLM observability are your daily vocabulary. You know how to build for — and communicate with — highly analytical stakeholders.You Orchestrate AI Tools, You Don’t Just Use Them: Claude Code or Cursor is your IDE. LangGraph or CrewAI is your architecture. You’ve shipped at least one production agentic system and you know when ‘accept all’ is safe — and when it isn’t.You Own Outcomes, Not Tasks: You take abstract requirements and turn them into working systems. You close loops, handle edge cases, and don’t need hand-holding to drive a project from kickoff to go-live.You Know When Done Means Shipped — and When It Means Reliable: You match engineering rigor to context. A well-scoped experiment gets validated fast; a client-facing system gets observability, guardrails, and a clear failure mode. You don't over-engineer proofs of concept — and you don't under-engineer production.Why This Role?Frontier Work, Not Filler: You aren’t building another chatbot. You’re designing AI systems that power investment decision-making and portfolio analytics for serious VC and PE firms.Real Autonomy: Once aligned on goals, you own delivery end-to-end. No process layers between you and the work. You are the conductor of your AI coding stack and the architect of your workflow.Senior Collaboration: You’ll work directly with the CEO and client stakeholders — technical strategy conversations with people who can execute on them. Small team, real decisions, no filler meetings.Upside Tied to Output: Compensation is uncapped in the ways that matter — your impact on client outcomes directly shapes what you earn and how fast you grow here.Must-have: Proven, hands-on experience shipping production AI/LLM systems used by real users — not an internal demo or hackathon project.Must-have: Advanced proficiency in an AI-native coding workflow — Claude Code, Cursor, Codex, or alternatives as your primary development environment, not a plugin you occasionally enable.Expertise in at least one domain with broad proficiency across the entire stack (infrastructure, backend, data, frontend). Preferred stack: Terraform, Python, Snowflake, React.Hands-on with LLM APIs, prompt engineering, RAG systems, and agentic frameworks (LangChain, LangGraph, CrewAI, Agno, or equivalent).Strong spoken and written English — you communicate complex technical trade-offs clearly