AROUNDTOWN Commercial Properties (ATCP) is the asset management
platform for Aroundtown SA, the largest listed commercial real estate
company in Germany. ATCP acts as owner representative thus managing,
leasing and optimising the commercial real estate portfolio, which, in
addition to typical office properties, also includes industrial,
logistics and retail properties. In addition to our company
headquarters in Berlin, Aroundtown Commercial Properties holds branch
offices in Munich, Düsseldorf, Frankfurt, Hanover and Leipzig. Our
employees of various nationalities and their performance stand for the
success of our company. Why This Role Exists We are building an
AI-native delivery team that ships working software in days, not
quarters. This role is for someone who lives in AI-native tooling —
Claude Code, Cursor, Replit, v0, Lovable, Bolt — and who treats
shipping as the unit of progress. This is not a Staff Engineer role
and is not asking for one. We are not asking for five-year
architectural ownership; the integration engineering function owns
that. We are asking for speed, judgment, and the ability to ship
working software end-to-end at high cadence. What Success Looks Like
Working software in users' hands fast — days and weeks, not
quarters. A portfolio of small, useful internal tools, automations,
and tenant-facing PoCs accumulates quickly. Some land, some get
killed. Both are signal. Tight feedback loops with real users —
internal teams, operations, pilot tenants. Prototypes that prove
themselves are handed cleanly to the integration engineering function
for hardening — without rewrite drama. Code that did not earn its
place is thrown away without ceremony. Responsibilities Building &
Shipping Build working software end-to-end in AI-native tooling:
Claude Code, Cursor, Replit, v0, Lovable, or equivalent. You are
fluent across at least several of these and you experiment with new
ones. Ship to small user groups fast — internal users, operations
teams, pilot tenants. Your loop is build → demo → learn →
rebuild. Cover the stack pragmatically: frontend, backend, basic data,
basic deploy. You do not need to be the deepest in any one layer; you
need to be effective across all of them. Build LLM-powered features
into products where they create real user or operational value —
AI-assisted search, document processing, intelligent summarization,
agentic workflows. Quality Calibration Write deliberately loose code
in early stages; tighten what proves itself. Recognize when something
is a five-day experiment and when it is a candidate for production.
Throw away what did not work without attachment. Double down on what
you did. Hand winning prototypes to the integration & hardening
engineering function with clean context — what it does, who uses it,
what it touches, what is not yet production-ready. Apply judgment on
quality calibration. Prototypes deserve different rigor from
production code; both deserve thought. AI-Forward Workflow Use AI
coding assistants as your default workflow — code generation, test
generation, refactoring, documentation, debugging — not as a
novelty. Continuously evaluate new AI development tools as they
emerge. Adopt fast when they earn it. Discard when they do not.
Maintain critical judgment over every AI output. Speed without
judgment produces bad code at scale. Your Profile Experience &
Expertise 4+ years of professional development experience or an
equivalent demonstrated portfolio of shipped work. Hard requirement: a
working portfolio of AI-built prototypes — at minimum 2 to 3 things
shipped recently using AI-native tooling. A link is required in the
application. Effective across the stack: frontend (React, Vue, or
similar); backend (Node, Python, or similar); basic database work; and
deployment to platforms like Vercel, Railway, Supabase, or
equivalents. Daily practice with AI coding assistants — Claude Code,
Cursor, or comparable. Not awareness, not occasional use — default
workflow. Working understanding of when something needs to be hardened
versus when it should stay in prototype form. Bonus if you have made
that judgment call in production before. Comfort with throwaway code
and rapid iteration. You do not require comprehensive test coverage on
a 3-day experiment. Real estate, proptech, or property operations
context is an advantage — not a gating requirement, but it
accelerates you. Core Competencies Velocity Under Uncertainty : You
ship in days even when the spec is fuzzy. You treat ambiguity as a
signal to build, not to delay. Prototype-to-Production Fluency : You
know which corners can be cut and which cannot. You know when to call
in the integration engineer. AI Tool Mastery : You are deep in at
least one AI coding environment and competent in several. You are not
a tourist. User Proximity : You stay close to the people using your
work. The feedback loop is short and direct. Comfort with Throwing
Code Away : You do not get attached. The portfolio matters more than
any single artifact. Pragmatic Quality : You apply the right level of
rigor for the stage. Experiments and production code get different
treatment by design. Who You Are Builder : You measure yourself in
things shipped, not story points moved. Allergic to Long Planning
Cycles : You would rather build a small bad thing on Tuesday than plan
the perfect thing for two weeks. Comfortable with Messy Code in
Service of Learning : You also know when to clean it up — or throw
it out entirely. Curious About the Domain : You actively want to
understand how a property gets managed, what an operations team does
day-to-day, and where the friction lives. AI-Forward : AI tools are
your default, not a novelty. You experiment with new ones every month
and have opinions about them. Discreet : You handle tenant data and
operational information responsibly, including data passing through AI
systems. Qualifications Working portfolio of AI-built artifacts
shipped in the last 90 days — link required in a