Mikhail Gorelkin

Principal AI Scientist & AI Systems Architect | Mathematician | Consultant

Framingham, MA, USA | Email: magorelkin@gmail.com

AI consultant with 21+ years of experience transforming complex, ill-defined business problems into production-grade engineering solutions for global enterprises and early-stage startups. I work end-to-end in AI: from defining the right business problem through production architecture to hands-on engineering. Original cross-disciplinary research is a source of competitive advantage in my work — not a separate activity from delivery.

• AI Systems Design & Architecture
• Generative AI / LLMs, RAG systems, LLM-Algorithm Integration, LLM Reliability, Agentic AI
• Artificial Intelligence, Natural Language Processing, Machine Learning, Deep Learning, Graph Neural Networks, Reinforcement Learning, Probabilistic Programming, Automated Decision Making
• Data Science, Forecasting, Optimization, Recommender Systems
• Complex Systems / Multi-Agent Modeling, Cybernetics / Intelligent Adaptive Systems
• Mathematical Modeling, Computer Science Algorithms
• Languages: Python, C++, C#, Scala, Julia, SQL
• Core Gen-AI & Agents: Hugging Face, LLMs, Agent Development Kit (ADK), AutoGen, crewAI, LangChain, Agent2Agent (A2A) and MCP protocols, Faiss, Pinecone
• Clouds: Google Cloud / Vertex AI, Amazon SageMaker, Amazon EC2 / Linux
• Other Tools: PyTorch, TensorFlow / Keras, scikit-learn, spaCy, PySpark, Akka, Mesa, Pyro, PyMC
• IDEs: Cursor and Visual Studio; AI Coding Agents: Codex

• From Case-Level to System-Level AI: Agentic Governance as the Next Frontier of AI-Native Automation, Aug 2026
• From Studying Complex Systems to Engineering Them in the Era of Agentic AI: The Complexity Threshold, Semantic Coupling, and the Governability Boundary, Aug 2026
• Superintelligence from Complexity, Apr 2026
• The Root Problem of LLM Hallucinations on the Turing Machine, Apr 2026
• Category Theory as a Language for Understanding Large Language Models (LLMs), Mar 2026
• A Categorical X-Ray for Complex Agentic Systems, Feb 2026
• A Complete AI-Driven Architecture for Enterprise Agentic Systems, Jan 2026
• Why Today’s “Agentic AI” Isn’t Truly AI (and How We Can Fix It), Aug 2025

• Cognitively-Augmented Specification Design (CASD): A methodology for complex problems.
• The Bidirectional Semantic Bridge: A configurable neuro-symbolic fusion architecture for LLM-algorithm systems, enabling integration from single-pass to compositional pattern algebra.
• Intelligence Fusion + Cognitive Augmentation: A framework that transforms data into multi-lens interpretations, guiding decision-makers through complexity to solutions aligned with their intuition.

Gorelkin AI Consulting & Advisory,  Boston, MA 04.2005 – Present
Principal AI Scientist & AI Systems Architect | Mathematician

• Selected clients include Visa, Ford, ServiceNow, Deloitte, Boston Consulting Group, Fractal Analytics, IEEE, Scientific Systems Company, AES, Celsius, airSlate, ADΣXT, NOHOLD, and Boston Biotech Clinical Research.

FieldGoal,  Los Angeles, CA 09.2025 – 02.2026
Principal AI Systems Architect

• Created the Native-AI approach to agentic systems and designed the enterprise architecture for the company’s platform, including a cognitively augmented decision layer to replace traditional reports and dashboards.

Simply Adaptive, London, United Kingdom 01.2010 – 07.2010
Interim Director

• Co-developed a strategic consulting framework and initial offerings for intelligent adaptive systems in financial services.

Compuware Corp., Technology Department, Detroit, MI 03.2000 – 11.2004
Software Developer VII

• Researched and developed advanced performance and scalability features for QALoad, helping the company win major enterprise clients, including Bank of America. Developed a statistical method for identifying server scalability bottlenecks using the Kruskal-Wallis test and modified Hodges-Lehmann estimators, and prepared the work for publication.

Central Transport, Sterling Heights, MI 08.1996 – 02.2000
Systems Architect

• Led a team of ten engineers in developing an NT-based distributed enterprise architecture for terminals across the US, Canada, and Mexico.

Voronezh State University,Voronezh, Russia
Master of Science, Mathematics

• Focused on Topological Methods in Nonlinear Functional Analysis.

• MCP: Build Rich-Context AI Apps with Anthropic, Anthropic, 2025
• Multi AI Agent Systems with crewAI, Parts 1 & 2, crewAI, 2025
• AI Agentic Design Patterns with AutoGen, Microsoft & Penn State University, 2024
• Retrieval Augmented Generation (RAG), Coursera / DeepLearning.AI, 2025
• Train & Fine-Tune LLMs for Production, Intel, 2023
• Machine Learning in Production, Coursera / DeepLearning.AI, 2025
• Quantum Computation using Qiskit, IBM, 2022
• Quantum Computing, Coursera / Saint Petersburg State University, 2021
• Algorithmic Information Dynamics, Santa Fe Institute, 2018
• Parallel Programming in Scala, Coursera / École Polytechnique Fédérale de Lausanne, 2017
• Text Mining and Analytics, Coursera / University of Illinois at Urbana-Champaign, 2015
• Statistical Learning, Stanford University, 2014
• Mining Massive Datasets, Coursera / Stanford University, 2014
• Introduction to Dynamical Systems and Chaos, Santa Fe Institute, 2014
• Introduction to Complexity, Santa Fe Institute, 2013
• Game Theory, Coursera / Stanford University, 2013
• Natural Language Processing, Coursera / Columbia University, 2013
• Algorithms: Design and Analysis. Parts 1 & 2, Coursera / Stanford University, 2013
• Machine Learning, Coursera / Stanford University, 2012
• Model Thinking, Coursera / University of Michigan - Ann Arbor, 2012

• Categories for AI, DeepMind, 2023
• Introduction to Quantum Computing & Quantum Machine Learning, IBM, 2022
• Introduction to Agent-Based Modeling, Santa Fe Institute, 2020
• Bayesian Methods for Machine Learning, Coursera / National Research University Higher School of Economics, 2019
• Deep Natural Language Processing, University of Oxford & Google DeepMind, 2017
• Deep Learning (TensorFlow), Udacity / Google, 2016
• Approximation Algorithms. Parts 1 & 2, Coursera / École Normale Supérieure, 2016
• Functional Programming Principles in Scala, Coursera / École Polytechnique Fédérale de Lausanne, 2015
• Deep Learning for Natural Language Processing, Stanford University, 2015
• Neural Networks for Machine Learning, Coursera / University of Toronto, 2015
• Machine Learning: Reinforcement Learning, Udacity / Georgia Tech, 2014

• The Conference on Neural Information Processing Systems, 2020
• The International Conference on Probabilistic Programming, 2018
• O'Reilly Artificial Intelligence Conference, 2016-2018
• IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO), 2007