Khaled EL TANNIR

As a lecturer at McGill University, I teach the flagship “Data at Scale” course, part of the Professional Development Certificate in Data Science and Machine Learning. This program combines theory with hands-on practice, preparing professionals to master the full data pipeline using modern tools and real-world case studies. 📊 Course Highlights: • Topics include Hadoop, Spark, Hive, Trino, Superset, NiFi, Kafka, Cassandra, Spark ML, and GraphFrames. • Hands-on workshops built on real industry datasets and use cases, bridging the gap between academia and practice. • Designed and distributed a custom sandbox virtual machine (Hadoop, Spark, NiFi, Kafka, Superset, Generative AI tools) used by 2,000+ students, recognized as an innovative teaching approach. • Mentored students from diverse industries, empowering them to apply Big Data & AI techniques directly in their work. This role reflects my passion for teaching, knowledge transfer, and building the next generation of data and AI professionals. --- https://www.mcgill.ca/continuingstudies/program/professional-development-certificate-data-science-and-machine-learning

At DataXper, I founded a consultancy dedicated to helping organizations accelerate their digital transformation through Big Data, AI, IoT, and Cloud solutions. Leveraging deep expertise in both technology and business, I partner with clients to design innovative architectures, implement proof-of-concepts, and build practical roadmaps that unlock measurable value. 🔹 What we do: • Define Big Data strategies, roadmaps, and action plans using our proven S.M.A.R.T. model. • Translate business needs into scalable architectures tailored to each organization. • Deliver POC/POV projects to validate and de-risk Big Data and AI initiatives. • Provide consulting and training on technologies such as Hortonworks, Cloudera, Hadoop, Spark, Kafka, and NiFi. • Offer technical support and coaching to strengthen in-house capabilities. • Educate teams through custom training programs, empowering them to adopt and maximize Big Data/AI tools. 💡 Our ambition: to become a driving force of digital transformation for our clients by combining innovation, expertise, and hands-on execution.

• Led the adoption of Generative AI across the organization by training and mentoring over 15 engineers in Prompt Engineering and LLMs, accelerating hands-on experimentation and knowledge transfer. • Designed and delivered a Proof of Concept AI Agent with RAG and a web-based interface (similar to ChatGPT) that enables engineers to query internal documentation and technical plans, significantly reducing time to access critical knowledge. • Created a comprehensive GenAI Capability Matrix, mapping AI opportunities across multiple business streams, which was formally adopted by senior leadership to guide AI strategy and innovation initiatives.

At Logient, I provided consulting and training services to help clients adopt Machine Learning and MLOps best practices in real-world production environments. 🔹 Key Contributions: • Acted as a trusted advisor, guiding teams in implementing and automating end-to-end ML workflows, from model development to deployment. • Delivered hands-on workshops focused on machine learning lifecycle management, CI/CD integration, and model monitoring. • Supported organizations in improving their data science pipelines, ensuring scalability, reliability, and governance. This engagement allowed me to combine technical expertise and mentoring, helping Logient’s clients operationalize ML solutions and establish a foundation for AI-driven transformation.

At Hydro-Québec, I played a key role in operationalizing machine learning at scale, transforming prototypes developed by data scientists into production-ready ML pipelines. 🔹 Key Achievements: • Built and automated end-to-end ML pipelines on Microsoft Azure ML and Azure DevOps, handling over 10 TB of data. • Designed and implemented a scalable MLOps framework, ensuring reproducibility, monitoring, and seamless deployment of models. • Established best practices for ML lifecycle management, improving collaboration between data science and infrastructure teams. • Partnered with IREQ (Hydro-Québec’s research division) to explore advanced optimization and predictive use cases. • Delivered a proof of concept demonstrating best practices and validating the value of automation in ML operations. This experience strengthened my expertise in enterprise-scale MLOps and showed the importance of bridging research innovation with real-world production environments.

At Cloudera, I partnered with enterprise clients to accelerate their adoption of Machine Learning and MLOps through Cloudera’s product suite. My role combined technical expertise, client engagement, and solution design, helping organizations unlock the value of their data with modern AI platforms. 🔹 Key Contributions: • Led presales engagements, demonstrating the value of Cloudera’s Machine Learning (CML) and Data Platform (CDP) to technical and business stakeholders. • Designed and delivered proofs of concept (POCs), showcasing how to build scalable ML pipelines, optimize workflows, and deploy models in production. • Acted as a trusted advisor on best practices for MLOps, including reproducibility, automation, and governance of ML workflows. • Promoted Cloudera products by evangelizing modern data and AI architectures, bridging the gap between technical capabilities and business outcomes. This role strengthened my ability to translate client needs into scalable AI/ML solutions, while demonstrating Cloudera’s unique value in the competitive ML ecosystem.

* Advising European clients on the industrialization of Big Data and Machine Learning solutions. * Provided consulting and training services on Big Data and Machine Learning best practices to help clients modernize their analytics and automation capabilities. 🔹 Key Contributions: • Advised on scalable data processing strategies and automation of regression models in production environments. • Delivered custom training sessions focused on data engineering, ML workflows, and model deployment. • Supported clients in optimizing their Big Data pipelines, improving performance, and operationalizing predictive analytics. This engagement strengthened my international consulting profile and showcased my ability to translate advanced AI and ML concepts into practical, production-ready solutions.

As a consultant with Enedis, I contributed to the modernization of their data and AI infrastructure, focusing on Big Data platform migration and MLOps best practices. 🔹 Key Achievements: • Designed a migration plan and roadmap to transition the Hive metastore and data (Hortonworks 2.6) to an Impala metastore on Cloudera CDP 7.1.4 (private cloud), ensuring scalability and continuity. • Acted as ML Architect/Engineer, defining and implementing MLOps frameworks, roles, and technologies to streamline the machine learning lifecycle. • Advised on best practices for DevOps and MLOps, enabling automation, reproducibility, and governance of ML workflows. • Supported cloud and hybrid infrastructure initiatives leveraging AWS and private cloud environments.

At iA Financial, I advised and guided the organization in its adoption of Machine Learning and Big Data platforms on Microsoft Azure, combining MLOps best practices with modern cloud-native analytics. 🔹 Key Achievements: • Acted as trusted advisor to define and implement MLOps best practices (DevOps principles applied to ML) on Microsoft Azure ML and Azure DevOps, enabling scalable and reliable ML model deployment. • Designed and delivered custom training paths in Data Science, Python, Azure Databricks, and Machine Learning, upskilling teams and accelerating adoption of advanced analytics. • Served as Big Data Architect, defining the reference architecture and patterns for a hybrid analytics platform on Azure, supporting both real-time (Kafka) and batch processing. • Helped establish a foundation for predictive modeling, fraud detection, and advanced analytics within the organization.

At Croix Bleue, I was responsible for designing and guiding the implementation of a next-generation DataHub platform to modernize analytics and enable real-time data-driven decision-making. 🔹 Key Achievements: • Designed a decoupled, event-driven DataHub architecture leveraging Azure Data Lake Gen2, Azure Databricks (Spark/Scala), Kafka, NiFi, MongoDB, MySQL, Atlas, and Ranger, enabling scalable and secure data flows. • Defined and implemented a security strategy with Role-Based Access Control (RBAC) and Tag-Based Access Control (TBAC) to ensure compliance and data governance. • Acted as technical advisor and mentor, supporting internal teams in adopting modern Big Data practices. • Conducted a comparative study of data governance tools (Collibra, Informatica, IO-Tahoe) to support enterprise adoption. • Delivered multiple proofs of concept (POCs): o Spark-to-MarkLogic integration for advanced analytics. o Real-time event ingestion, processing, and storage pipeline using NiFi, Kafka, Spark, MongoDB, and MySQL. This role allowed me to combine hands-on architecture design with strategic advisory and governance leadership, laying the foundation for an enterprise-scale Big Data ecosystem.