Imad Ez

• Automated PR/MR Analysis: Designed and implemented standardized ML engineering workflows using AI Agents intercept pull/merge requests before pipeline execution, analyzing code quality, security vulnerabilities, and architectural impact across repositories. • MLOps Best Practices & LLM-Driven Development Pipeline: An LLM hosted on Amazon Bedrock generates detailed code changes descriptions, identifies potential issues, and provides contextual improvement suggestions before merge approval. Documented best practices and patterns for team adoption. • Pipeline Optimization: Built Langgraph agents to predict failures and optimize CI/CD, suggests optimized test strategies, and predicts pipeline failures, reducing unnecessary builds and accelerating deployment cycles. • Skills: Amazon Bedrock, Claude LLM, Langraph, CI/CD (GitHub Actions, Jenkins), AWS, Code Analysis, MLOps Best Practices, Pipeline Automation

• Production Voice Assistant & Knowledge Graph RAG: Architected and deployed low-latency voice assistant supporting voice and text interactions with knowledge graph integration for events/organizations—established best practices for semantic search, document retrieval, and real-time optimization. • Multi-LLM Strategy & Cloud Deployment: Designed a dynamic LLM selection framework based on task complexity, deployed on Azure with CI/CD pipelines (Azure DevOps), containerization (Docker), and infrastructure-as-code. • LLMOps Best Practices: Built end-to-end LLMOps workflow including prompt versioning, model evaluation, cost optimization, and performance monitoring • Skills: Real-time systems, Knowledge Graphs, RAG, LLMOps, Azure, Multi-LLM

• Production-Grade RAG System: Designed and deployed an advanced RAG system for sales assistance using multi-indexing and chunking strategies—implemented monitoring and optimization practices for production reliability. • LLM Agent Engineering: Developed an intelligent agent with planning and reasoning capabilities for sales automation, implementing best practices for prompt engineering, context management, and error handling. • API Integration & Documentation: Built automated tools for PowerPoint generation with seamless Salesforce, Slack, and email integration—created technical documentation and usage guides for stakeholders. • Data Pipeline & Processing: Leveraged Databricks for data processing and feature engineering in the RAG system, optimizing data workflows for production deployment. • Skills: Azure, Databricks, Vertex AI, NLP, RAG Architecture, Agent-Based Systems, API integration

• Data Pipeline Development: Built end-to-end data pipelines for credit risk analytics, implementing data quality standards and validation practices. • Production Data Platform: Deployed data models on Dataiku, optimized large-scale data reconciliation workflows using Spark, implemented data versioning, monitoring, and automated data quality checks. • Skills: Python, Spark, SQL, Hive, Dataiku, Data Pipeline Orchestration, Data Quality

• Automate the identification of the root causes for abnormal systems in a monitoring platform • Analysis of system metrics (time series) and discovering data structure using AI

Development and optimization of software tools dedicated to optical calibration applied to cinema and television professions for virtual production: EZprofile