Eric C.

Most ML and data projects are optimized for the demo, not for what happens once real users, real data volumes, and real business processes depend on them. I build differently because I come from production environments. Before this I spent years inside Visa on the data side of one of the largest payment networks in the world, where nothing shipped without being production ready and sloppy data was never an option. Today I bring that same bar to enterprise teams building on the Microsoft stack. I design the data foundation, build and deploy the ML and AI layer, and connect it to the systems the business actually runs on: Dynamics 365, Power Platform, and Azure. 𝗪𝗵𝗮𝘁 𝗧𝗵𝗮𝘁 𝗟𝗼𝗼𝗸𝘀 𝗟𝗶𝗸𝗲 𝗶𝗻 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 A model that scores well in a notebook is not a product. I build the whole path from raw data to business outcome: ingestion and pipelines that hold up under real load, models that are versioned, monitored, and retrainable, and delivery into the tools people already use, whether that is a Power App, a Dynamics 365 workflow, or a Power BI report. That includes modern AI work like LLM integrations, RAG pipelines, and agent workflows, built with the same discipline around error handling, data integrity, security, and cost as everything else, not bolted on as a gimmick. 𝗪𝗵𝗲𝗿𝗲 𝗜 𝗙𝗶𝘁 𝗜𝗻 Data engineering: Azure Data Factory, Synapse, Fabric, and Databricks pipelines, plus schema design, query performance, and data quality built to hold up under real load. ML and MLOps: Training, deployment, and monitoring on Azure ML and open-source tooling, with MLflow, CI/CD, and retraining built in from the start. AI and LLM solutions: Azure OpenAI, Copilot Studio, RAG, and agent workflows, grounded in your data and wrapped in proper guardrails. Power Platform and Dynamics 365: Dataverse modeling, Power Apps, Power Automate, Power BI, and embedding ML predictions directly into D365 and the business processes around it. Architecture and technical leadership: Setting standards, reviewing designs, and mentoring teams so what ships is maintainable long after the first release. If you need ML and data systems that work inside your real business stack, not next to it, let’s talk about what it takes to get them live. 𝗧𝗲𝗰𝗵 𝗜 𝗪𝗼𝗿𝗸 𝗜𝗻 Microsoft and Azure: Power Platform (Power Apps, Power Automate, Power BI, Copilot Studio), Dynamics 365, Dataverse, Azure ML, Azure OpenAI, Azure AI Foundry, Data Factory, Synapse, Fabric, Azure Functions, Azure DevOps Languages: Python, SQL, TypeScript, JavaScript Data engineering: Databricks, Spark, dbt, PostgreSQL, MongoDB, Redis, Supabase ML and deep learning: PyTorch, TensorFlow, scikit-learn, XGBoost, Keras, HuggingFace Transformers, OpenCV LLMs and agents: GPT-4o, Claude, Gemini, LLaMA 3, Mistral, DeepSeek, LangChain, LangGraph, CrewAI, OpenAI Agents SDK, MCP, A2A RAG and vector databases: Azure AI Search, Pinecone, ChromaDB, FAISS, Weaviate, Qdrant, pgvector, Neo4j for GraphRAG LLMOps: LoRA and QLoRA fine-tuning, LangSmith, W&B, Guardrails, vLLM, MLflow Backend and APIs: FastAPI, Node.js, Django, Flask, REST and GraphQL Frontend: React, Next.js, Streamlit, Chainlit, Tailwind CSS Automation: Power Automate, n8n, Make, Zapier, webhook pipelines Cloud and infra: Azure (primary), AWS including Bedrock and SageMaker, GCP including Vertex AI, Docker, Kubernetes, CI/CD