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AI Manager | Artificial Intelligence

Profile summary

Career highlights

Key skills

Professional experience

Defines analytics strategy and steers machine learning initiatives for enterprise clients. Explores and understands complex data to shape advanced predictive, prescriptive, and optimized solutions.

• •Supports clients in extracting insights and intelligence from data using machine learning and artificial intelligence techniques.
• •Collaborates as part of a cross-functional team across all phases of analytics projects, including defining questions and designing solutions.
• •Executes research and development tasks for advanced data solutions.
• •Implements and tests analytical models during project execution cycles.

Shaped enterprise conversational AI systems and cloud infrastructure, steering system architecture across Azure environments. Directed engineering efforts for retrieval augmented generation pipelines, vector databases, and governance frameworks without relying on external dependencies.

• •Designed and deployed an enterprise-grade conversational AI architecture using Azure OpenAI, Azure Bot Service, and serverless Azure Functions.
• •Reduced inference cost by 20-35% through prompt optimization and token-efficiency strategies.
• •Integrated Collibra connectors to discover physical data layers and associate them with business glossary terms, enabling end-to-end data lineage and governance.

Reported internally while advising engineering departments on production deployment standards, steering technical operations for machine learning automation frameworks without direct personnel ownership.

• •Trained technical teams on production environment deployment workflows.
• •Advised internal groups on best practices for machine learning process automation.

Reported to the regional leadership team, overseeing client-facing engineering initiatives and enterprise engagements involving large-scale data systems. Directed technical alignment with enterprise architecture requirements while providing advisory oversight for framework adoption across client accounts.

• •Guided clients through the implementation of MLOps frameworks using Cloudera CDP and CML.
• •Designed scalable machine learning pipelines and optimized cloud-native deployment strategies.

• •Trained engineering personnel on best practices for processing large data volumes.

• •Delivered production ready machine learning solutions by migrating data scientist prototypes into Azure ML pipelines managing 100 TB of data.
• •Designed and implemented a scalable MLOps framework with continuous integration and continuous deployment Azure pipelines.

• •Designed and deployed a dedicated analytics environment using Azure Databricks, Kafka, and Data Lake for data science teams.
• •Developed custom training programs for employees covering Machine Learning, Python, and cloud-native analytics.

• •Built real-time processing proofs of concept integrating Spark, NiFi, and MongoDB to test streaming data capabilities.
• •Evaluated Collibra and Informatica through a comparative governance study to assess enterprise data management options.

• •Designed data ingestion architecture handling millions of daily events with NiFi, Kafka, Hadoop, Spark, and Hive.
• •Authored enterprise-wide Kafka security roadmap incorporating RBAC and TBAC using Atlas, Ranger, and Knox.

• •Defined migration strategy from Cloudera to AWS, securing data pipelines and analytics.
• •Designed real-time market prediction platform using AWS SageMaker, Athena, and Spark.

• •Defined and designed a Big Data strategy and governance framework.
• •Developed IoT-based analytics use cases applying machine learning to audience and operations data.

• •Delivered workshops on Big Data tools including Hadoop, HDFS, Hive, Impala, Flume, and Sqoop for technology professionals.
• •Led a sentiment analysis hands-on workshop using Twitter data to demonstrate practical text processing techniques.

• •Optimized Hadoop clusters on Azure HDInsight to improve data processing performance.
• •Advanced machine learning and internet of things use cases, deploying image recognition systems with Hololens and Amazon Echo.

• •Executed a sentiment analysis proof of concept using big data processing tools.
• •Trained solution architects on the big data ecosystem, including Hadoop, Spark, Hive, and Flume.

• •Delivers instruction on distributed systems and cloud architectures Using Hadoop, Spark, Kafka, and Trino for professional learners.
• •Facilitates applied artificial intelligence coursework covering large language models, retrieval augmented generation, and intelligent agents.

• •Taught courses on distributed systems and data pipelines in French to enrolled students.
• •Delivered instruction on Hadoop, Spark, Kafka, Flume, Hive, and Impala.

Education

Intelligent STOP Sign System (Edge AI, Computer Vision, IoT)

• Dissertation focused on AI-driven safety systems, intelligent transportation, autonomous decision systems combining computer vision, IoT sensing, radar inputs, and embedded machine learning.

Computer Science

• Specialization in distributed systems and applied data mining.

Electronics

• Specialization in electronics and automation - 1992

Languages