Professional Experience

Building production AI systems and data infrastructure

Data & AI Platform Engineer

Robodata

January 2026 — Present

Team & Domain

Platform Engineering / AI & Data Infrastructure — building the core platform that enables privacy-first, self-service data and LLM capabilities for enterprise clients.

Key Technical Responsibilities

  • •Designed and implemented multi-tenant data pipeline infrastructure using a medallion architecture (Bronze/Silver/Gold), with automated pipeline generation driven by versioned metadata definitions
  • •Built LLM-powered data discovery and schema inference workflows, where local language models sample ingested datasets to automatically classify fields, detect PII/GDPR-relevant data, and generate metadata contracts
  • •Developed Infrastructure-as-Code (IaC) provisioning templates for cloud-native environments (Azure, Kubernetes), including automated deployment of LLM inference stacks (Ollama + OpenWebUI), storage, compute, and VPN networking
  • •Implemented metadata-driven ETL/ELT pipelines in Python/PySpark/SQL covering source ingestion, structural normalization, sensitivity masking, and data lineage tracking across Bronze/Silver/Gold layers
  • •Designed and exposed AI-ready data interfaces (Machine Context Protocols / MCPs) enabling LLM agents to safely query governed datasets with built-in privacy enforcement

Technologies Used

PythonPySparkSQLApache SparkAirflowAzureKubernetesTerraformDockerOllamaOpenWebUIMCPsSQLAlchemyWireGuard

Notable Achievements

  • •Built the platform's LLM-powered data onboarding layer for automated schema discovery and PII classification
  • •Architected a zero-access, privacy-first AI infrastructure running entirely within customer cloud tenants
  • •Reduced data onboarding complexity from bespoke ETL projects to metadata-declared, auto-generated pipelines
  • •Established multi-tenant data isolation patterns with environment-scoped resource provisioning

AI/Data Engineering Skills Involved

Production LLM integration (Ollama, K8s)
NLP/data pipeline intersection
Data systems at scale (Medallion, DAGs)
MLOps/AI infrastructure (IaC)
Privacy-aware AI engineering (GDPR)

Operational Trader

QuantFi

November 2022 — May 2023

Key Technical Responsibilities

  • •Developed and optimized ML-driven trading strategies (Random Forest, XGBoost, Neural Networks, walk-forward cross-validation) in Python, achieving a steady 5% lift in daily PnL through backtesting, tick-level feature engineering, and short-term volatility forecasting.
  • •Automated the end-to-end model pipeline, from SQL/Python data ingestion and retraining to daily forecast publishing and Matplotlib performance dashboards, significantly reducing manual prep time.

Teaching Assistant

Vrije Universiteit Amsterdam

June 2022 — November 2024

Key Responsibilities

  • •Taught weekly Probability Theory tutorials for first-year students, developing collaborative teaching methods and educational materials alongside faculty to explain complex statistical and mathematical concepts.
  • •Built a student portfolio system for first-year Econometrics students to ease university transition, and represented the Bachelor programme at Open Days and promotional events.