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.

Role: AI-Native Engineer (Full-Stack / Agentic AI Engineer)
Location: Remote / Hybrid (Warsaw)
Role Type: Individual Contributor / Hands-on Delivery

The Opportunity
Every 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 ownership of outcomes, not demos. 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.

Key Responsibilities:

• Agentic AI Systems: Multi-step LLM workflows, RAG pipelines, and agent orchestration systems, owned from architecture to production.
• 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.
• Data Platform Engineering: Scalable pipelines and cloud infrastructure (AWS/GCP) that underpin AI features, vector databases, data ingestion layers, API integrations.
• Technical Discovery & Client Engagement: Translate business needs into AI-first technical proposals, in the room with CFOs, GPs, and operating partners.
• AI Quality & Internal Standards: Guardrails, automated testing, and observability for AI systems. Help define internal engineering standards.

Requirements:

• 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.
• 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.
• Ability to run AI initiatives with limited support, from discovery to delivery, often across multiple client engagements in parallel.
• Experience in fintech, private capital (VC/PE), or healthcare data systems is a strong plus.
• Familiarity with data engineering stacks (Snowflake, dbt, Airflow, AWS data services) is a strong plus.