Asks for

The ad says“Data & Storage: Per-tenant Aurora MySQL in isolated subnets, S3 for document persistence, and an append-only ledger for auditability.”
The ad says“AI Integration: AI orchestration deeply integrated into application landscape via AWS Bedrock, e.g., to extract information and drive agentic activity.”
The ad says“Background processing: Containerized worker services and event-driven AWS Lambda orchestrators for email ingestion, document triaging, and action running.”
The ad says“Dev Workflow: uv, Ruff, ty, pytest (unit, integration, property-based), Docker, GitHub Actions, and heavy use of Agentic IDEs to accelerate feature shipping.”
The ad says“Core Backend: Python 3.14, FastAPI, Pydantic, and SQLAlchemy 2.0 in a strict four-layer architecture, exposed as separate internal and external APIs.”
The ad says“Dev Workflow: uv, Ruff, ty, pytest (unit, integration, property-based), Docker, GitHub Actions, and heavy use of Agentic IDEs to accelerate feature shipping.”
The ad says“AI-native: Real experience building LLM/agentic tooling, and understanding how to bound probabilistic output with deterministic code.”
The ad says“Core Backend: Python 3.14, FastAPI, Pydantic, and SQLAlchemy 2.0 in a strict four-layer architecture, exposed as separate internal and external APIs.”
The ad says“Technical proficiency: Deep proficiency in Python is mandatory, with strong command of REST API design, relational data modeling, and layered, service-oriented architecture.”
The ad says“Frontend: React single-page app for the internal workbench.”
The ad says“Technical proficiency: Deep proficiency in Python is mandatory, with strong command of REST API design, relational data modeling, and layered, service-oriented architecture.”
The ad says“Technical proficiency: Deep proficiency in Python is mandatory, with strong command of REST API design, relational data modeling, and layered, service-oriented architecture.”
The ad says“Data & Storage: Per-tenant Aurora MySQL in isolated subnets, S3 for document persistence, and an append-only ledger for auditability.”
The ad says“Technical proficiency: Deep proficiency in Python is mandatory, with strong command of REST API design, relational data modeling, and layered, service-oriented architecture.”
The ad says“Core Backend: Python 3.14, FastAPI, Pydantic, and SQLAlchemy 2.0 in a strict four-layer architecture, exposed as separate internal and external APIs.”

The Context

Banks still run their most critical, regulated work by hand; reading letters and emails, re-keying data between systems, chasing down missing info. It's the kind of work that's too important to get wrong and too tedious for anyone to want to do.

Porters fixes this. Our AI agents run these regulated processes end to end, with the audit trails and security banks actually require, so the work moves at the speed customers expect instead of the speed a backlog allows.

We're just over a year old and already live with real customers, growing the product quickly, and were named Fintech of the Year 2026. The goal is to build the operating system for autonomous banking. We're backed by Earlybird, one of Europe's oldest venture firms, and Seedcamp, who wrote first checks into Revolut, Wise, and UiPath.

The Mission

You'll own the outcomes. You take an ambiguous business problem and turn it into clear requirements, a design with clear business rationale, sequenced work, and a realistic estimate, build the hardest parts yourself, and multiply a team of mid-level engineers with well-defined, extensible work and the reviews that keep quality high. You stay an IC at heart while making the work legible, sized, and worth doing.

This job is in person in Zurich, or remote in Barcelona or Berlin.

Your responsibilities

Deliver the highest-risk work yourself: Be the IC where it matters. Personally engineer the most complex, business-critical components, from cross-service logic to the boundary where AI outputs become deterministic, auditable actions, and set the technical bar for the team as you go.
Turn business goals into shipped projects: Take an ambiguous problem and own it through to delivery, defining requirements, a design that starts from customer impact and takes the highest-ROI path whether it's make or buy, sequenced work, estimates, and clear engineering acceptance criteria a team can execute with minimal churn.
Design complex systems that don’t break down in 12 months: Architect entire systems so the likely next requirement plugs in cleanly through configuration or a new adapter, keeping components reusable and loosely coupled, separating business logic from shared services, and maintaining a single authoritative, append-only source of truth for critical data - including consideration of make-or-buy options.
Scale the team's autonomy and quality: Set decision thresholds so engineers self-serve low-stakes calls, define sanctioned paths for experimenting with regulated data, and spread understanding of our systems to cut key-person risk. Settle design in review, then build the road to production: CI/CD with quality gates, automated migrations, and systems that report their own health.
Wow our customers: You solve for the customer's actual problem, not just the request, and measure success by customer outcomes. What would it take for the customer to say “Porters made my day!”?

Your profile

Experience: Typically 8+ years of professional software engineering, with a track record of shipping in fast-paced, ambiguous environments. Regulated industry and/or VC-backed startup experiences are a plus.
IC strength: A hands-on builder who ships the hard things, and who naturally thinks in business outcomes, work packages, ROI, and extensibility.
Project definition: Proven ability to write requirements and designs from business context, break them into estimated, sequenced work, and drive a project to completion in a tool like Linear.
Technical proficiency: Deep proficiency in Python is mandatory, with strong command of REST API design, relational data modeling, and layered, service-oriented architecture.
AI-native: Real experience building LLM/agentic tooling, and understanding how to bound probabilistic output with deterministic code.
Natural leader: You lift the people around you through design review, coaching, and clear direction, and you enjoy doing it.
Going the extra mile: Be part of a high-growth phase, where we move fast and expect focused intensity. 9-to-5 is not the mode we work in, if you want to build something that matters with a team that ships, we are your people.

What We Offer

Ownership of whole product areas: Latitude to shape what we build and how we scope, sequence, and ship it, with a team to build it with.
Founder collaboration: Work directly with founders who have built and scaled fintech and analytics products before.
We win and lose together as a team: We expect people to leave their comfort zone on a daily basis, however, we work together as a team and support each other at any time.
Growth: A dedicated development budget, free to use for whatever gives you a level-up.
Culture: Strong focus on a healthy, high-performance team culture, ranging from regular team events and dinners to an inclusive, fast-paced team that grows together.

Our stack

Core Backend: Python 3.14, FastAPI, Pydantic, and SQLAlchemy 2.0 in a strict four-layer architecture, exposed as separate internal and external APIs.
Background processing: Containerized worker services and event-driven AWS Lambda orchestrators for email ingestion, document triaging, and action running.
AI Integration: AI orchestration deeply integrated into application landscape via AWS Bedrock, e.g., to extract information and drive agentic activity.
Data & Storage: Per-tenant Aurora MySQL in isolated subnets, S3 for document persistence, and an append-only ledger for auditability.
Platform: A multi-tenant AWS environment on ECS Fargate with strict per-tenant network and data isolation (VPC, NACLs, WAF), CloudFront + Cognito (Google SSO/JWT) and API Gateway (IAM) at the edge, defined as code in CloudFormation across dev/staging/prod accounts.
Frontend: React single-page app for the internal workbench.
Dev Workflow: uv, Ruff, ty, pytest (unit, integration, property-based), Docker, GitHub Actions, and heavy use of Agentic IDEs to accelerate feature shipping.