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.

Connect on LinkedIn Watch a recorded talk

Next conference

15–16 October 2026

Berlin, Germany

GenAI Conference

Cost-Effective AI Infrastructure You Own
Confirmed

More upcoming conferences

Previous talks

30 September 2026

Munich, Germany

Redis AI Roadshow

Scaling AI employees in an enterprise environment
Previous conference
9 July 202615:30–16:00 CEST

Berlin, Germany

WeAreDevelopers World Congress

Agentic employees in world's most downloaded FinTech app
Talk delivered

Topics 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.

Sasa Fajkovic
Download JPG · 1181 × 1074 · 197 KiB Download WebP · 1181 × 1074 · 83 KiB

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.

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.

Base: Berlin, Germany.

Connect with me

Connect on LinkedIn