# Amine El Farssi
Senior AI Engineer (GenAI / Agentic Systems) • Data Scientist • Cloud Architect  
Leuven, Belgium • amineelfarssi@gmail.com • +32 488 46 71 51  
Links: LinkedIn • GitHub • Portfolio

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

## Experience

### AI Engineer (GenAI / Agentic Systems) — KBC Bank & Insurance (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).
- 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: prompt-injection guardrails, policy enforcement in system prompts, and red-teaming to identify vulnerabilities pre-release.

### Data Scientist (AML / Graph ML) — KBC Bank & Insurance (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 (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.

### Big Data Engineer — JEMS Group (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.

### Data Scientist — Bioceanor (Valbonne, France) | Jul 2020–Mar 2021
- Built time-series forecasting models (LSTM) for sea-water quality signals (turbidity/chlorophyll/temperature), deployed via API and integrated with LoRa-based sensor pipelines; evaluated with MAE.

## Education
- MSc Applied Data Science — Data ScienceTech Institute (DSTI), France (2020–2021)
- Diplôme d'Ingénieur (Civil Engineering) — Strasbourg, France (MSc equivalent)

## Certifications
- AWS Certified Solutions Architect – Associate (2022)

## Skills
- Agentic / GenAI: AI Agents, RAG Systems, LangChain, LangGraph, Bedrock, Guardrails, eval harnesses (golden datasets)
- AWS: Bedrock, AgentCore, Lambda, API Gateway, EventBridge, SQS, DynamoDB, ECS/ECR, IAM, S3
- Data & MLOps: PySpark/Spark, Airflow, MLflow, Docker, OpenTelemetry (OTEL), CI/CD
- Programming: Python, SQL, TypeScript, Bash, Terraform (HCL)

## Languages
French (Native) • English (Fluent) • Arabic (Fluent) • Dutch (B1)