AI & ML Engineering

Welcome to my profile! I'm Reza Fazeli, a Machine Learning Engineer at IBM with over 8 years of experience in the field. I have a Master of Applied Science in Mechanical Engineering from the University of Toronto. Throughout my career, I have worked at top tech companies like IBM and SoundHound Inc., where I have honed my skills in machine learning and AI. In addition to my industry experience, I have also served as a Data Science Instructor at Juno College of Technology and the University of Toronto School of Continuing Studies. I have coached more than 50 individuals in the past, helping them break into the exciting world of AI and ML. If you're looking to build a career in this field or need guidance on specific projects, feel free to reach out to me. Let's work together to achieve your goals!

Welcome to my profile! With a PhD in Industrial Engineering from the University of South Florida and extensive experience as an Applied Scientist at Amazon, I bring a wealth of knowledge in AI and machine learning to my coaching. I've led teams to develop cutting-edge systems that have generated significant revenue gains and operational efficiencies. My background includes designing advanced models for fraud detection and search engine optimization, as well as teaching and research in applied statistics and machine learning. Having coached 18 individuals, I am passionate about helping others excel in AI/ML engineering. Let's work together to achieve your career goals in this dynamic field!

I bring hands-on experience in AI, machine learning, data science, and production-grade GenAI engineering, with a focus on building practical solutions that solve real business problems. My experience includes designing and implementing AI-powered applications, RAG systems, LLM-based analytics tools, and agentic solutions that help teams automate workflows, retrieve knowledge, and generate insights from enterprise data. I have built agentic AI solutions using different frameworks and implemented them in production environments. I have worked across the full lifecycle, from problem definition and prototype development to model integration, evaluation, deployment, and scaling. My experience also includes implementing key production components such as memory, observability, traceability, human-in-the-loop workflows, evaluation, and guardrails. I can help customers understand not only the concepts behind AI and ML engineering, but also the practical architecture and implementation steps needed to move from prototype to reliable production-ready AI systems.

𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗮𝗹 𝗔𝗜 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁 | 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 & 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 | 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 | 𝗔𝗜 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 I design and scale enterprise-grade AI architectures — production-ready Generative, Agentic, and Multi-Agent AI platforms. With 12+ years across platform engineering and distributed systems, my work focuses on where AI moves from prototype to enterprise: governed, observable, scalable, and cost-controlled. At Zoom, I contributed to AI platform initiatives bridging developer ecosystems with modern AI. Today, I architect reusable platform foundations for agent orchestration, lifecycle governance, distributed execution, and scalable deployment. 🔐 𝗔𝗜 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 & 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗦𝗮𝗳𝗲𝘁𝘆 A core thread across my work is making AI systems audit-ready and policy-bound — designing authentication and authorization layers (Azure AD / Entra ID, OAuth2, IAM SigV4, JWT) with pluggable AuthProvider patterns, multi-account topologies for tenant isolation, cross-account observability (ADOT + PrivateLink), and cost governance through gateway routing and credit-based metering. Enterprises don't just need AI that works — they need AI that's auditable, cost-controlled, and policy-bound. That's the layer I architect. 🤝 𝗦𝘁𝗮𝗿𝘁𝘂𝗽 & 𝗙𝗼𝘂𝗻𝗱𝗲𝗿 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 I partner with startups to design AI-native products from day one — system design, platform strategy, multi-agent workflows, LLM integration — helping founders move from concept to scale without crippling technical debt. 🎓 𝗠𝗲𝗻𝘁𝗼𝗿𝘀𝗵𝗶𝗽 & 𝗖𝗼𝗻𝘁𝗲𝗻𝘁 Through YouTube (youtube.com/codeztech), atiqueahmed.com, codeztech.in, and GitHub (github.com/codeztech-atique), I've mentored 20+ developers into top companies — covering Hugging Face, Ollama, PyTorch, Node.js, React, Angular, AWS, and system design. 🔹 𝗖𝗼𝗿𝗲 𝗙𝗼𝗰𝘂𝘀 𝗔𝗿𝗲𝗮𝘀 Generative AI (GenAI) & Agentic AI Multi-Agent Systems (MAS) LLM Architecture & RAG AI Governance, Lifecycle Management & Policy Enforcement Auth Architecture (OAuth2, SigV4, JWT, Entra ID) AI Platform Engineering & Distributed Systems Cost Governance & Cross-Account Observability Autonomous Workflow Design 🏆 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀 Guinness World Record holder (Programming Excellence) Designed scalable AI platforms used at enterprise scale Enabled startups to launch AI-powered MVPs rapidly Delivered technical workshops and architecture sessions If you're building the next generation of governed, enterprise-grade AI systems — 𝗹𝗲𝘁'𝘀 𝗰𝗼𝗻𝗻𝗲𝗰𝘁.

Welcome to my profile! With extensive experience as a Software Engineer at top tech companies like Google DeepMind, Meta, and Google, I specialize in AI/ML engineering, particularly in model serving infrastructure and optimization. My journey began with a strong foundation in Computer Science from Rose-Hulman Institute of Technology and Boston College. Over the years, I've coached numerous individuals, helping them break into competitive AI/ML roles by leveraging my industry insights and technical expertise. Whether you're looking to refine your skills or navigate the complexities of AI/ML engineering, I'm here to guide you every step of the way. Let's connect and chart your path to success!

I'm a researcher at the University of Cambridge working on AI for neuroimaging. I have a PhD in Computational Neuroscience and nearly 10 years of experience building ML models across healthcare, energy, and tech. I've worked with everything from deep learning and computer vision to time-series forecasting and fraud detection. I can help you understand AI concepts, build models, or solve data science problems in a practical way.

I have worked with large enterprise customers such as KPMG and CIBC, as well as small to mid-sized startups, on developing and governing AI agents. I work with several of the Anthropic and OpenAI tools.

Welcome to my profile! With over a decade of experience in AI and machine learning, I have honed my skills at top companies like Verizon and UnitedHealth Group, where I led projects in agentic & generative AI, recommendation systems, and workforce demand forecasting. My journey from a System Development Engineer at Infosys to a Sr. Specialist Data Science Engineer at Verizon has equipped me with a deep understanding of AI/ML applications across various industries. I have successfully coached numerous individuals, helping them break into competitive AI/ML roles by leveraging my expertise in natural language processing, data science, and cloud platforms. Whether you're looking to refine your technical skills or navigate the complexities of AI/ML career paths, I'm here to guide you every step of the way. Let's connect and chart your path to success in the AI/ML field!

Welcome to my profile! With a Ph.D. from Rutgers University and extensive experience as a Senior Software Engineer at Google, I specialize in AI and machine learning engineering. My career has been dedicated to building robust data frameworks and models, from enhancing safety features at Uber to curating geo data at Foursquare. At Google, I focus on data understanding and discovery, leveraging graph technologies to drive insights at scale. Whether you're looking to break into AI/ML engineering or advance your career, I'm here to guide you through the complexities of this dynamic field. Let's connect and chart a path to your success!

Official Mastra AI Agents Ambassador, building production RAG pipelines, MCP server connectivity, and multi-agent orchestration systems for enterprise clients. Udacity Machine Learning Engineer nanodegree. Sixteen years of cloud architecture across GCP, AWS, and Azure. Production AI systems delivered for Accenture and Capco clients in highly regulated industries where failure has real consequences. The sessions teach the engineering decisions that keep AI systems running past month one. Ideal for : Software engineers, ML practitioners, and technical leads building AI systems that need to survive contact with real data