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.

• 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, Codex, or alternatives.
• 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 (e.g., LangChain, LangGraph, CrewAI, Agno).
• Strong spoken and written English communication skills.
• Ability to run AI initiatives independently 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, 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 engineering 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.
• Use Python/FastAPI for backends and React for frontends.
• Integrate with LLM providers such as OpenAI, Anthropic, Gemini.
• Use Claude Code or Cursor as primary development environment.

Data Platform Engineering:

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

Technical Discovery & Client Engagement:

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

AI Quality & Internal Standards:

• Define guardrails, automated testing, and observability for AI systems.
• Contribute to internal engineering standards that improve over time.