Houcem B.

• • Advised on AI modeling approaches and evaluated project designs for feasibility, scope, and business impact across multiple AI domains, including GenAI, anomaly detection, and forecasting.

• • Prioritized AI use cases across predictive maintenance and battery lifespan monitoring, reducing battery failure risk by 19% and accelerating testing cycles by 6%, enabling faster launches for next-generation vehicle models.

• • Designed agentic AI system ingesting XML, HTML, and datalake tables, leveraging MCPs to autonomously validate battery data and generate actionable insights, reducing manual data validation by 40%.

• • Mentored cross-functional team of 5 engineers on AI best practices, establishing standardized approaches for model benchmarking and deployment readiness across the organization.

• • Acted as primary strategic interface between technical delivery and executive leadership, directing agile delivery across technical team through sprint planning and backlog prioritization.

• • Established evaluation framework with KPIs and complexity scoring, translating findings for executives and non-technical stakeholders to enable C-level roadmap decisions and cross-functional alignment.

Tech & Tools: Python, scikit-learn, AI Agents, RAG, NLP, Streamlit, Pandas, Power BI, Jupyter, Confluence, Jira, PoC evaluation frameworks, KPI mapping techniques

• • Led end-to-end EV data pipeline modernization, migrating legacy ETL architecture from proprietary tools to open-source stack, achieving 40% improvement in processing speed and 50%+ cost reduction while maintaining zero downtime.

• • Architected a scalable AI platform with LLM-powered RAG chatbot for natural language querying of 500K+ battery test records, enabling 40+ engineers to self-serve instead of relying on 2 data specialists.

• • Conducted presales activities, including technical requirements gathering and solution design, evaluating multiple technologies, through POCs to recommend optimal semantic search architecture aligned with client needs.

• • Integrated CI/CD workflows and deployment automation for data infrastructure, ML models, and analytics components, reducing manual operational overhead by 35%.

• • Mentored engineering team of 3 on modern data engineering practices, LLM evaluation methodologies, and data governance, translating complex AI capabilities for non-technical stakeholders to secure buy-in for continued investments.

• • Fine-tuned a domain-specific LLM to improve machine translation accuracy for automotive industry jargon, enhancing multilingual communication across internal systems.

• • Managed end-to-end delivery of OCR automation solution, directing the digitization and structured extraction of data from scanned business documents to optimize operational and compliance workflows.

• • Delivered two production-ready AI services with 30% performance gains over baseline models, deployed as SaaS products for internal and external client use.