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Thomas L.TL

Thomas L.

AI Automation | Docs Generation | Agents, RAG, LLM

€550/day
Brussels, BE
8-15 years

Average response time: 1 hour

About Thomas

Teams spend hours on tasks a workflow could do. I help companies detect and fix business leaks through targeted workflow and AI automation, built around your constraints.

Where your business likely has leaks

- Time lost in repetitive processes:
Document generation, data extraction, report assembly, compliance formatting - tasks eating 20, 30, 40 hours a month, every month. Your team knows they are painful, but "it's always been done this way"

- Slow turnarounds costing deals
A client sends a 500-page spec and expects a quote within days. Your team spends a week manually cross-referencing parameters, normalizing terminology, and filling spreadsheets. Your competitor who automates this wins

- Operational friction blocking growth:
Every hour spent on manual processes is an hour not spent on clients, strategy, or growth. The real cost isn't the task - it's what your team would be doing instead

My approach

- Business first, technology second
I don't start with tools, I start with your business context, constraints, and objectives. The automation is custom-built to solve your specific problem, not to showcase technology

- Diagnostics
Identifying time leaks, cost leaks, and untapped revenue opportunities in your current workflows

- Design
Data confidentiality, human-in-the-loop validation, production reliability, team adoption

- Built to run
Containerized, monitored, maintainable

Recent projects

- AI-generated industrial/business documents (manufacturing SME):
A multi-agents workflow, data confidentiality, human in the loop, Docker deployment. Targets a 20-40 hours/month manual task (pilot outside Malt)

- AI-powered commercial quote generation from business specs:
Workflow includes flagging atypical requirements for human review and custom markup

- Hedge fund-grade systematic trading infrastructure:
Deployed on VPS for live execution across futures and CFDs

Github: ThomasLaloux
  • English

    Native or bilingual

  • French

    Native or bilingual

  • Dutch

    Conversational

Remote only
Primarily works remotely

Experience

  • Self-employed
    AI-Powered Workflow Automation
    February 2026 - Today (6 months)
    - Workflow/AI automation: multi-agentic workflow automation for document generation under industrial/business constraints. n8n orchestration, python-based multi-agents workflow (planner, writer, validator and iterative loops), cloud & local LLMs/SLMs, RAG extraction, Qdrant as vector DB, API integrations, prompt engineering.
    - Systematic Trading Infrastructure Developer: data collection and brokers interface: broker data sources; parquet files, DuckDB, orders and position management. Backtesting: custom engine, walk-forward optimization. Strategy development. Live trading engine.
    AI Automation Python n8n LLM RAG
  • Self-Employed
    Proprietary trading & quantitative development
    January 2018 - November 2025 (7 years and 10 months)
    - Eight years running my own book and building the systems behind it, by choice, not as a client-service practice. Developments now largely complete, opening to client work in 2026.
    - Algorithmic trading (python, mql5): developed and backtested mid-frequency algorithmic trading systems (long/short trend following, mean reversion), coded advanced day trading indicators to support discretionary trading decisions. Currently extending a systematic trading infrastructure in Python.
    - Data analysis & equity market screening (matlab): performed data analysis and supervised classification (comprehensive screening engine) to identify position trading opportunities across global equity markets on a daily basis (data acquisition with APIs, trend following strategies, custom visualization, parallel computing, deployment in production.
    - Proprietary trading: conducted position trading/investing in equity and crypto markets, and shared insights over 2024-2025 (metrics available upon request).
    Algorithmic Trading Python Equity Market Screening Project Management Quantitative Development
  • ENGIE
    Business & Market Analyst
    July 2012 - December 2017 (5 years and 5 months)
    Brussels, Belgium
    - Data analysis (SQL, python): applied SQL queries and ML classification techniques to cluster of clients behavior from smart thermostat data and produce energy efficiency insights.
    - Power market modelling: modelled the LT Italian supply, demand and power prices as a key driver for strategic positioning and investment files.
    - Consulting in economic modelling (excel modeling): designed total cost of ownership models comparing green mobility solutions across energy types. Cooperated with M&A teams on assets valuations. Managed budgets, relationships with internal clients, and VIEs.
    - Market analysis: studied green mobility market potential, competition & regulation.
    Project Management Data analysis Market modelling Consulting Python

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Education

  • Diploma of Advanced Studies in Statistics
    UCLouvain
    2006
    Diploma of Advanced Studies in Statistics
  • MSc. in Statistics
    UCLouvain
    2005
    MSc. in Statistics

Skill set

Categories