Dominik Stiftinger-Lang

Senior Backend & AI Systems Engineer

DominikStiftinger-Lang

I build production Go systems, AI-assisted delivery infrastructure, and trading software where correctness is financially material.

Available 1 September 2026 30–40 hours/week Remote EU
Selected work Download CV
Profile

Profile

Systems engineering for consequential software.

Nine years across production software, quantitative finance, and applied mathematics. My work sits where architecture, financial correctness, and delivery discipline meet.

Based in
Graz, Austria
Engagement
B2B via Austrian FlexCo
Languages
German, English

Selected work

Production systems, delivery infrastructure, and quantitative research.

Representative work from ChainBrain, confidential professional deployments, and published research. Client-sensitive details are deliberately excluded.

01 / 06

AI-Assisted Software Delivery & Quality Control

AI-Assisted Engineering Lead / Systems Architect · 2026–present

Designed bounded multi-agent workflows for implementation, codebase research, test generation, financial-flow audits, and independent verification. Reusable skills, structured handoffs, and evidence-based branch verdicts make AI-generated changes reviewable in a financially sensitive Go monorepo.

  • Claude Code
  • OpenAI Codex
  • MCP
  • Go
  • Python
  • GitLab CI
Auditable agent delivery for financial systems
  1. 01Task packetAcceptance criteria, financial risk
  2. 02Owned worktreesSpecialist agents, file boundaries
  3. 03QC lanesRace, unit, debt, reuse review
  4. 04Branch verdictPASS / CONCERNS / FAIL
Financial-flow auditsIndependent verifierReusable skills & handoffs

02 / 06

Distributed Portfolio Management & Trading Platform

Co-Founder, Lead Backend Engineer & Architect · 2022–present

Architected production Go services spanning portfolio construction, account state, execution, reconciliation, reporting, and client-facing APIs. The system uses durable workflows and idempotent financial processing to operate safely across regulated and hosted boundaries.

  • Go
  • Temporal
  • PostgreSQL
  • gRPC
  • OpenAPI
  • Docker
ChainBrain demo interface showing current and rebalanced portfolio allocations
Demo environment: portfolio allocation and rebalancing workflow

03 / 06

Multi-Venue Order Management & Execution

Lead Engineer · Confidential professional deployment · 2024–2026

Built five exchange integrations, order-lifecycle state machines, fill and balance reconciliation, exposure controls, and execution-quality measurement. The production deployment achieved sub-1 bps median implementation shortfall without exposure-limit breaches.

  • Go
  • REST
  • WebSocket
  • PostgreSQL
  • Temporal
  • gRPC
Order-group execution process with reserve, placement, fill, and reconciliation states
Order-group execution and reserve lifecycle

04 / 06

Portfolio Optimization Architecture

Systems Architect / Quantitative Engineer

Designed the service path from public and private market data through portfolio optimization to multi-venue rebalancing. Explicit data and execution boundaries keep strategy computation separate from account state and exchange-specific behavior.

  • Market data
  • Optimization
  • Account state
  • Execution
  • Reconciliation
Architecture connecting market data, databases, portfolio optimization, rebalancing, and exchanges
Portfolio construction and execution service boundaries

05 / 06

Multi-Factor Investment Framework

Researcher / Lead Author · Crypto Valley Association Research Journal · 2025

Developed a layered allocation framework combining asset-specific, systematic, and subjective inputs with risk constraints and execution. The paper was the best-rated of twelve submissions to the journal issue.

  • Factor models
  • Black-Litterman
  • Risk constraints
  • Portfolio construction
  • Python
Multi-layer investment process combining asset-specific, systematic, and subjective factors
Published multi-layer investment process

06 / 06

Quantitative Strategy Research

Quantitative Research & Model Validation

Built reproducible research pipelines to compare allocation approaches across market regimes. Evaluation covered cumulative returns, drawdowns, and risk-adjusted measures rather than treating a single backtest metric as sufficient evidence.

  • Python
  • PostgreSQL
  • Time series
  • Backtesting
  • Risk analysis
Cumulative return comparison of portfolio allocation approaches from 2021 to 2025
Cumulative return comparison, 2021–2025

Services

Focused support for systems that have to hold up in production.

01

AI-Assisted Engineering Systems

Agent workflows, reusable skills, MCP integrations, quality gates, and operating models for teams adopting coding agents without losing reviewability.

02

Distributed Backend & Financial Systems

Go services, durable workflows, APIs, data boundaries, reconciliation, and exchange integrations for high-consequence production environments.

03

Architecture & Technical Due Diligence

Architecture reviews, failure-mode analysis, delivery risk assessment, and concrete implementation plans grounded in the existing system.

Experience

From quantitative research to production ownership.

2026–present

AI-Assisted Engineering

Engineering Lead / Systems Architect

2022–present

ChainBrain FlexCo

Co-Founder, Lead Backend Engineer & Architect

2019–2022

Nerox GmbH

Head of Quantitative Finance, later Chief Risk Officer

2020–2021

Graz University of Technology

Research Assistant, Theoretical Computer Science

2017–2019

Nerox GmbH

Software Developer

Contact

Available for a substantial engineering engagement from September.

Contract work is the preferred model. Permanent roles are considered selectively as a fallback.

dominik.lang@chainbrain.fi +43 664 5009429 LinkedIn