30 September 2026 12:00–16:00 CEST
Confirmed Conference talks built from real work.
I share practical lessons from building AI systems and leading engineering teams. My sessions focus on the decisions, trade-offs and operating changes behind software that works in production.
Next conference
More upcoming conferences
15–16 October 2026
Confirmed 4 November 2026
Pending details 19 November 2026
Confirmed 3–4 December 2026
Confirmed Previous talks
9 July 2026 15:30–16:00 CEST
Berlin, Germany
WeAreDevelopers World Congress
Agentic employees in world's most downloaded FinTech appTopics I can shape for your audience.
These are practical talk directions that can be adapted to a conference session, podcast or leadership roundtable.
Managing hybrid teams of people and AI employees
For: Leaders introducing AI agents into day-to-day operations
Moving from an impressive agent demo to a defined role that human colleagues can understand, evaluate and trust.
Attendees will leave with
- Define an AI employee's responsibilities and access before choosing its tools
- Evaluate the work with deterministic checks and human review
- Keep human ownership visible as people and AI employees work together
Giving agents production access
For: Platform, security and engineering leaders
Giving an agent enough access to be useful without losing control of approvals, evidence or recovery.
Attendees will leave with
- Scope permissions around a job rather than a broad capability
- Place approval boundaries where consequences become material
- Design audit and recovery paths before increasing autonomy
AI infrastructure you can operate and own
For: AI, platform and technical decision-makers
Choosing infrastructure that keeps cost, control and operational responsibility understandable after launch.
Attendees will leave with
- Map workloads to infrastructure instead of following a default stack
- Compare managed and self-hosted options through cost and control
- Treat monitoring, failure and maintenance as design inputs
Autonomous feedback and self-improvement for AI systems
For: AI, platform and product teams operating AI in production
Building feedback processes that help AI systems improve from real outcomes without obscuring ownership or quality decisions.
Attendees will leave with
- Separate feedback signals from decisions about system changes
- Evaluate proposed improvements before promoting them
- Keep auditability and human accountability visible throughout
Scaling engineering teams with clear ownership
For: Engineering managers, directors and growing product teams
Growing delivery capacity without making responsibilities, decisions and technical direction harder to understand.
Attendees will leave with
- Make decision ownership explicit as the organisation grows
- Scale the operating model alongside team size
- Use structure to support autonomy rather than adding ceremony
Leadership through technical and organisational change
For: Engineering leaders and cross-functional teams
Leading change while keeping the purpose, trade-offs and expectations clear for the people doing the work.
Attendees will leave with
- Connect technical choices to outcomes people can understand
- Create feedback loops before a change becomes difficult to reverse
- Keep accountability visible across team boundaries
Human drive and a culture of proactive feedback
For: Engineering leaders, managers and teams navigating change
Creating a culture where people actively ask for, give and act on useful feedback instead of waiting for formal review cycles.
Attendees will leave with
- Make feedback routine, timely and specific
- Model the proactive behaviour expected from the team
- Connect individual growth with stronger team outcomes
Business process optimisation before automation
For: Business, operations, product and technology leaders
Improving a workflow before software or AI makes its existing friction faster and harder to change.
Attendees will leave with
- Map how work happens before choosing a tool
- Remove unnecessary steps and hand-offs before automating
- Measure the outcome that the redesigned process should improve
For event organisers.
My first-person background, current headshot and LinkedIn profile for event planning and introductions.
Short biography, in my own words
I lead Applied AI at Trade Republic, where I am also a Staff Engineer. Based in Berlin, I set technical direction, grow engineering teams and stay hands-on with architecture and production AI systems. I share practical lessons about leadership, hybrid human and AI teams, infrastructure ownership and responsible adoption. I’m also the founder of Changify and the creator of agents-skill-eval and unraid-rsync.
Long biography, in my own words
I’m an Applied AI Lead and Staff Engineer based in Berlin. At Trade Republic, I set technical direction for applied AI, grow the team and work hands-on with the architecture and software behind production systems. My work connects engineering, product and business outcomes. I focus on the leadership, evaluation practices and governance needed for people and AI employees to work together responsibly. Before focusing on applied AI, I led engineering teams in banking, built B2B and B2C software products and taught programming and productivity courses to ~500 people. I founded Changify, created the open-source projects agents-skill-eval and unraid-rsync, and spoke at WeAreDevelopers World Congress 2026.
Base: Berlin, Germany.