Alain Bansais

As Head of a Generative AI team, I design and deploy advanced AI solutions, including but not limited to Retrieval-Augmented Generation (RAG), to solve complex business challenges in a secure, high-performance Azure environment. My role blends technical expertise, strategic vision, and operational team leadership. Key responsibilities: – Design and implement scalable AI architectures (RAG, agents, orchestration systems, evaluators) – Build Model Context Protocol (MCP) frameworks to coordinate model-system interactions – Integrate LLM Judges to assess, filter, and refine AI-generated outputs – Optimize performance, cost efficiency, and output relevance across Gen AI systems – Lead and mentor a specialized Generative AI engineering team Tech stack: – Cloud & Infra: Azure Cognitive Search, Azure Functions, AKS, Azure OpenAI, Blob Storage – Data & Backend: REST APIs, relational & NoSQL databases, ETL pipelines – AI & NLP: GPT models, embeddings, fine-tuning, MCP orchestration, LLM-based evaluation systems Strategy & delivery: – Identify and prioritize high-value AI use cases beyond RAG, including intelligent agents and adaptive workflows – Collaborate with product and business teams to ensure functional alignment and measurable ROI – Define and monitor KPIs to track performance and impact of deployed solutions Leadership: – Drive technical rituals, architectural reviews, and continuous improvement initiatives – Stay ahead of emerging trends in AI, cloud, and orchestration protocols – Promote AI-first engineering principles and best development practices

As Co-founder and CTO of Circl, an innovative mobile application dedicated to grocery price comparison, I led all technical and product aspects of the project, combining both strategic and operational expertise. Technical leadership and product development: – Designed and implemented a high-performance technical architecture (Cloud Architect) – Deployed on Apple and Google Play stores, with performance monitoring and regular updates Technologies and tech stack used: – Mobile: SwiftUI (iOS), Kotlin (Android) – Backend: NestJs, FastAPI, robust APIs, DBT (Data Build Tool) – Cloud: Google Cloud Platform (GCP), full stack integration for a secure and scalable infrastructure – AI: LLMs via API (OpenAI, Mistral, Google, Anthropic) Product strategy and user experience: – Clear product vision: precise feature definition and continuous backlog refinement – Product-Market Fit validation through wireframing, prototyping, and regular user testing – Ongoing optimization of key performance indicators (KPIs) Digital communication and marketing strategy: – Actively involved in defining and executing the social media strategy (Instagram, TikTok) – Effectively highlighted the product's value, boosting brand visibility and user engagement This unique combination of technical expertise, strong product vision, and experience in digital communication enabled me to build an innovative, high-performing product aligned with market expectations.

As a Lead AI/MLOps Manager for 6 years in a specialized consulting firm, I supported a wide range of clients—from startups to large international corporations—in the industrialization of advanced Artificial Intelligence and Big Data solutions. Technical leadership and project management: – Agile project management and delivery oversight of cross-functional teams – Design and implementation of robust, scalable, and secure platforms – Definition of technical architectures tailored to client needs Expertise in Data Science and Big Data: – Development of advanced Data Science models (Python, Spark) – Automation of model training and deployment pipelines (CI/CD) – Optimization of large-scale data processing workflows Technologies and methodologies used: – Languages & Frameworks: Python, Spark – Infrastructure & Automation: Microsoft Azure, Docker, Ansible, GitLab CI/CD – Version Control: Git This extensive experience enabled me to deliver fast and efficient data-centric solutions aligned with MLOps best practices.

Technical Delivery Lead of multiple ML/Data Use cases (~60 persons)

Lead of a Pizza team (6-7 p) for the development and industrialization of models of deep learning and computer vision.

Continuous integration / Deployment of predictive AI's models on multiple hosts using Ansible, and testing with molecule

Constrained optimization / Python / models production deployment (XgBoost, RandomForest, ..) / Feature Engineering / Data Exploration

Stacks: Scala / Spark / Hadoop / Hive / Cassandra Tech lead of a pizza team (3-4 p), implementation of the data workflow from the dev environment to the production launch.