Curriculum Vitae

Professional Summary

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Senior AI Engineer with 5 years of AI/ML/DS experience, currently building and hardening production AI agents at one of Belgium’s largest banks. Strong focus on agentic systems, AWS architecture, and operational excellence (evaluation, observability, and security).

Professional Experience

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AI Engineer (GenAI / Agentic Systems) | KBC Bank & Insurance

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Leuven, Belgium | 2023 - Present

• Built and shipped production AI agents for core operations, including an insurance claims-handling agent that automated information gathering and follow-ups; improved handler throughput by ~2–3× across ~300 claims/month (target claim type).
• Designed an event-driven agent runtime with state synchronized to enterprise backends using EventBridge (decoupled integration) and DynamoDB (state management) to meet reliability and consistency constraints.
• Delivered an autonomous mortgage/credit review agent that retrieves and applies credit policies/SOPs, integrates with CRM dispatching, supports human-in-the-loop, and handles ~150 questions/month (refinancing, variable-rate reviews, simulations).
• Built a reusable RAG platform on AWS enabling business units to ingest policies/FAQs, generate vector snapshots, and serve consistent retrieval for downstream agents and assistants.
• Implemented AgentOps + observability: golden datasets as CI/CD deployment gates, post-deploy evaluation for prompt changes, OTEL tracing/logging, and circuit breakers for integration failures.
• Led security hardening for agentic apps: prompt-injection guardrails, policy enforcement in system prompts, and red-teaming to identify vulnerabilities pre-release.

Technologies: Python, AWS (Bedrock, AgentCore, Lambda, EventBridge, SQS, DynamoDB, API Gateway, ECS/ECR, IAM), Docker, Terraform, OpenTelemetry (OTEL)

Data Scientist (AML / Graph ML) | KBC Bank & Insurance

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Leuven, Belgium | 2021 - 2023

• Built AML transaction-monitoring models using an ensemble (GBM / Random Forest / XGBoost) combined with graph-based scoring (diffusion over transaction/customer networks) to increase hit-rate and reduce investigation time versus rule-based detection.
• Engineered network features (e.g., PageRank, Jaccard similarity, neighborhood overlap) and applied community detection (Leiden) to surface suspicious clusters and patterns beyond single-transaction signals.
• Operated the system with governance-aligned thresholding (≈~2% of transactions flagged) on high-volume transaction flows (order of magnitude: 10^6/day).
• Implemented daily batch scoring pipelines using PySpark + Airflow, with experiment tracking and monitoring in MLflow.

Technologies: Python, PySpark/Spark, XGBoost, SQL, MLflow, Airflow, Graph analytics

Big Data Engineer | JEMS Group

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France | Mar 2021 - Aug 2021

• Migrated and standardized data pipelines on Databricks using the medallion architecture (bronze/silver/gold), delivering a clean curated layer ready for analytics warehousing.

Technologies: Databricks, Spark, SQL, Python

Data Scientist | Bioceanor

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Valbonne, France | Jul 2020 - Mar 2021

• Built time-series forecasting models (LSTM) for sea-water quality signals (e.g., turbidity/chlorophyll/temperature), deployed via API and integrated with LoRa-based sensor pipelines; evaluated with MAE.

Technologies: Python, TensorFlow/Keras, LSTM, Time series, APIs, IoT/LoRa

Education

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Master of Science in Applied Data Science

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Data ScienceTech Institute (DSTI) | France | 2020 - 2021

Focus: Machine Learning, Deep Learning, Statistical Modeling, Big Data Technologies

Diplome d’Ingenieur in Civil Engineering

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Strasbourg | France

Equivalent to Master of Science. Foundation in analytical thinking, mathematical modeling, and complex systems engineering.

Certifications

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• AWS Certified Solutions Architect – Associate (2022)

Technical Skills

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Languages

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