AI & Full-Stack Engineer

AI & Full-Stack Engineer — Frigg

• Full-time · Zug, Switzerland · Hybrid · Competitive salary + PSOP

The mission

Build the technical core of a platform that turns unstructured project data into investor‑grade analysis — and help define how AI changes the way infrastructure deals get done.

About Frigg

Frigg is building the operating system for renewable energy infrastructure investment. Our platform helps private market investors run deal pipelines, screen projects against investment criteria, conduct structured due diligence, and collaborate with developers and deal teams. We are a 10‑person team which sits at the intersection of AI, private markets, and the energy transition.

The role

This is a senior individual contributor role on a small, fast‑moving team. You will own Python‑based data and AI pipelines end‑to‑end, contribute to the full‑stack across C#/.NET and Next.js/React, and help design features that translate raw project data into outputs investors can act on. You will work directly with the founders and be a key voice in technical architecture decisions.

What makes this role special: you will need to understand why the analysis matters — the logic of an infrastructure investor, the income streams of a solar or wind project, what a technical advisor looks for in a data room. The best candidate is someone who bridges that world and the engineering world.

What you'll build

• Document ingestion and normalization pipelines (PDFs, spreadsheets, unstructured data rooms)
• Pre‑screening engines that check projects against configurable investor criteria
• Graph‑based querying of platform data to surface relationships and patterns across deals
• Yield validation, financial benchmarking, and risk scoring modules
• Automated document generation (summaries, memos, structured outputs)
• AI/LLM integrations across the full product workflow

What you'll contribute to

• Full‑stack features in C#/.NET (backend) and React / Next.js (frontend)
• Azure‑hosted infrastructure and deployment pipelines
• Technical architecture decisions alongside the founding team

Requirements

• 3–5 years of experience in software or data engineering (production code, not just notebooks)
• Strong Python — you've shipped pipelines that run reliably in production
• Hands‑on experience integrating LLMs and AI tooling into real products
• You understand the language of infrastructure investors: project income streams, key financial metrics, the due diligence process for renewable energy assets
• Working knowledge of C#/.NET and Next.js/React
• Curiosity about graph databases (experience is a bonus; genuine interest is required)
• English fluency

If you do not meet 100 % of the requirements but you think that you are still a good fit, please apply anyways!

Nice to have

• Direct experience in asset management, energy project finance, or technical advisory
• Financial modelling for infrastructure assets (solar, wind, storage, grid)
• Familiarity with document parsing libraries and data room workflows

Tech stack

Aspire · Python · C#/.NET · React · Next.js · Azure · LLM APIs · pandas / NumPy · Cosmos DB · document parsing libraries. GitHub‑based workflow with code review, CI/CD via GitHub Actions.

What we offer

• Competitive salary benchmarked to Swiss tech startups
• Stock option plan (PSOP) participation
• Flexible, hybrid work setup based in Zug (couple days / week in office)
• Direct access to founders and real architectural ownership
• Small team, fast decisions — your work ships and matters immediately
• Mission at the intersection of AI, private markets, and the energy transition

Interview process

• Screening: CV, GitHub, and screening questions
• Meeting #1: background & cultural fit exchange
• Meeting #2: applied interview with a short take‑home assignment and/or live challenge
• Meeting #3: meet the team
• Decision

How to apply

To apply, please email your resume to jobs[at]frigg.eco with the subject line 'Application: AI & Full-Stack Engineer'.

In the email, please also answer the following 3 questions:

1. Describe a data pipeline or AI workflow you've built in production. What was the input, what was the output, and what broke along the way?
2. What is the most exceptional achievement in your career or life?
3. What excites you most about what Frigg is building and why now?

Finally, link your GitHub as well as your socials (LinkedIn, X, website, whatever you deem relevant).