AI-Native 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 $210B+ in assets. Our average engagement runs five years. Our NPS sits above 80.

• Proven, hands-on experience shipping production AI/LLM systems used by real users.
• Advanced proficiency in an AI-native coding workflow such as Claude Code, Cursor, or Codex as primary development environment.
• Deep expertise in at least one domain with broad proficiency across infrastructure, backend, data, and frontend; preferred stack includes Terraform, Python, Snowflake, React.
• Hands-on experience with LLM APIs, prompt engineering, RAG systems, and agentic frameworks (LangChain, LangGraph, CrewAI, Agno, or equivalent).
• Strong spoken and written English communication skills.
• Ability to independently run complex AI initiatives from discovery to delivery across multiple client engagements.
• Experience in fintech, private capital (VC/PE), or healthcare data systems is a strong plus.
• Familiarity with data engineering stacks such as Snowflake, dbt, Airflow, and AWS data services is a strong plus.

Agentic AI Systems:

• Own multi-step LLM workflows, RAG pipelines, and agent orchestration systems from architecture to production.
• Know when to move fast and validate versus when to engineer for scale.
• Deliver systems that real clients depend on for investment decisions.

Full-Stack AI Applications:

• Build client-facing web applications with AI embedded throughout.
• Develop Python/FastAPI backends and React frontends integrated with LLM providers (OpenAI, Anthropic, Gemini).
• Use Claude Code or Cursor as the primary development environment.

Data Platform Engineering:

• Build scalable pipelines and cloud infrastructure (AWS/GCP) underpinning AI features.
• Manage vector databases, data ingestion layers, and API integrations.

Technical Discovery & Client Engagement:

• Translate business needs into AI-first technical proposals.
• Engage directly with CFOs, GPs, and operating partners.

AI Quality & Internal Standards:

• Define guardrails, automated testing, and observability for AI systems.
• Contribute to internal engineering standards across engagements.