CTO / Head of Engineering

Ship reliable systems. Even the AI ones.

I've spent 15 years stabilizing platforms where downtime costs money. Now I apply the same discipline to AI systems where hallucinations cost more than downtime — maritime decision-support, legal document retrieval, critical infrastructure. Ownership, metrics, reliability over heroics.

Best fit: B2B SaaS scaleups navigating growth — and teams shipping AI into production where accuracy, citations, and evaluation matter.

Dragan Lalos

Frankfurt, DE

CTO / Engineering Leader • Platforms & AI

Platform
Resilience under growth

Stability and performance as systems scale under real load.

AI Systems
Production-grade LLM

RAG, evals, and reliability practices for AI where accuracy is non-negotiable.

Org
Teams that ship

Clear ownership, delivery cadence, and accountability without bureaucracy.

Leadership

How I Lead Engineering

I build engineering systems that scale: ownership, reliability, and delivery cadence-so leadership can plan, product can commit, and customers feel stability.

Non-negotiables: ownership over activity, metrics over opinions, calm operations over heroics.

Systems

  • Architecture ownership

    Define target architecture and decision-making (ADRs) so teams move fast without rework.

  • Reliability as a function

    Implement SLIs/SLOs, error budgets, and incident response to cut critical incidents and improve resilience.

  • Delivery system

    Standardize CI/CD and infrastructure to make releases predictable, repeatable, and fast.

People & Alignment

  • Team scaling

    Hire, onboard, and mentor engineers; create clear roles, ownership, and growth paths.

  • Cross-functional alignment

    Align engineering, product, and stakeholders on priorities, trade-offs, and execution cadence.

Expertise

Tech focus

I use technology to produce outcomes: predictable releases, stable operations, and faster iteration.

Stack

  • TypeScript
  • React/Next.js
  • Node/Nest.js
  • GraphQL & REST
  • AWS · Terraform · Docker

Practices

  • System design & ADRs
  • SLIs/SLOs & incident response
  • CI/CD & release engineering
  • Observability & performance
  • Team enablement & standards

AI Engineering

  • Python, FastAPI
  • RAG architectures (ChromaDB, FAISS, pgvector)
  • OpenAI, Anthropic, local models (Ollama)
  • Embedding models for bilingual retrieval
  • Eval harnesses (RAGAS, custom) with golden datasets
  • Hallucination guardrails & citation-forced generation