Lead DevOps / AI Engineer with 8+ years of experience designing, building, and scaling production-grade data and AI platforms across fintech, e-commerce, and telecom. Deep expertise in Azure-centric data and AI stacks (Databricks, Delta Lake, Azure Data Factory, Airflow, Spark/Scala, Python, SQL), combined with hands-on delivery of GenAI and agentic AI systems (RAG, LightRAG, LLMs) in regulated enterprise environments.
Proven track record delivering end-to-end AI platforms at scale, processing 5TB+ of data and supporting 2M+ transactions per day, preventing €1.2M+ in annual fraud losses and enabling customers to launch AI-driven analytics and financial services generating €1.4M+ in revenue. Designed and operated an Agentic AI Data Platform that ingests large-scale enterprise data via robust ETL pipelines, transforms it into analytics-ready datasets, and delivers dashboards, insights, and AI-powered recommendations embedded directly into business workflows.
Core strengths:
Agentic AI • RAG • LLM Integration • Python • LangChain • LlamaIndex • TensorFlow • Vector Databases • Airflow • Databricks • Spark / Scala • Azure OpenAI • Vertex AI • MLflow / MLOps • Azure • Reliability & Observability
AI Text Humanization & Detection Bypass Platform
PureText converts your AI-generated content into fully humanized, undetectable writing—ensuring it passes every AI detection tool. Built with advanced NLP models and machine learning algorithms to create natural, human-like text that bypasses all major AI detectors.
Impact: 1k MRR, successful SaaS business
Real-time fraud detection using GPT-4 and ML models
Built a highly scalable fraud detection system processing 2M+ daily transactions with real-time anomaly scoring using GPT-4, embeddings, and vector databases.
Impact: Prevented €1.2M+ in annual fraud losses
ETL pipelines and analytics for 5TB+ monthly data
Designed robust ETL pipelines with Spark, Scala, and Airflow for processing massive amounts of customer and transactional data, enabling BI dashboards and compliance reporting.
Impact: Reduced processing failures by 55%, cut processing time by 30%
Legacy monolith to cloud-native microservices
Refactored legacy monolith into containerized microservices on Kubernetes with Istio for service discovery, mTLS encryption, and A/B testing capabilities.
Impact: 99.99% uptime for 50k+ req/min, 60% faster provisioning
Automated security scanning and compliance
Integrated automated security scans into CI/CD pipeline with SonarQube, OWASP ZAP, and custom LLM-based scripts, ensuring PSD2 and GDPR compliance.
Impact: 85% reduction in production CVEs, 92% test coverage
Lead DevOps / AI Engineer with 8+ years of experience designing, building, and scaling production-grade data and AI platforms across fintech, e-commerce, and telecom. Deep expertise in Azure-centric data and AI stacks (Databricks, Delta Lake, Azure Data Factory, Airflow, Spark/Scala, Python, SQL), combined with hands-on delivery of GenAI and agentic AI systems (RAG, LightRAG, LLMs) in regulated enterprise environments.
Proven track record delivering end-to-end AI platforms at scale, processing 5TB+ of data and supporting 2M+ transactions per day, preventing €1.2M+ in annual fraud losses and enabling customers to launch AI-driven analytics and financial services generating €1.4M+ in revenue. Designed and operated an Agentic AI Data Platform that ingests large-scale enterprise data via robust ETL pipelines, transforms it into analytics-ready datasets, and delivers dashboards, insights, and AI-powered recommendations embedded directly into business workflows.
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