Senior AI/ML Engineer and Tech Lead

Bryan David Maione

Fuquay-Varina, North Carolina

+1-919-***-**** **************@*****.*** https://www.linkedin.com/in/bryan-david-m-1b4b9041/

Summary

Senior AI/ML Engineer with over ten years of expertise in AI-oriented full-stack development.Delivered 40% improvements in clinical, consumer marketing, and financial analytics accuracy, 70% automation of manual workflows, and achieved consistent platform availability exceeding 92% by architecting end-to-end AI pipelines and agentic systems. Proven leader and mentor, having built and guided high-performing engineering teams over extended periods while enabling enterprise clients to scale safely under standards such as HIPAA.

PROFESSIONAL EXPERIENCE

Viz.ai Oct 2023 – Dec 2025

Tech Lead/ML Engineer Boston Massachusetts

•Architected and delivered a scalable AI-driven healthcare analytics platform processing 20M+ Medicare cost reports, leveraging Python, Azure Databricks, and low-latency data pipelines for real-time fraud detection, reducing false positives by 30% and accelerating processing.

•Designed structured evaluation suites and automated benchmarking using Python, LangChain, LlamaIndex, and Hugging Face evals, supporting zero-shot, few-shot, and system-prompt scenarios; integrated Salesforce, Google Sheets, and internal workflows for data enrichment and QA automation.

•Implemented agent-to-agent (A2A) communication protocols to coordinate AI agents for complex clinical and financial workflows, increasing throughput by 40%.

•Designed and deployed scalable multi-agent AI frameworks using LangGraph, AutoGen, LangChain, enabling autonomous collaboration and task orchestration, reducing processing cycles by 30%.

•Led design and implementation of distributed NLP pipelines with Hugging Face Transformers on PySpark, achieving 40% faster patient document processing and 25% higher classification accuracy.

•Developed MCP-compliant AI agent toolchains and real-time computer vision (CV) React/Redux interfaces for clinical monitoring, improving system robustness and reducing notification response time by 25%.

•Engineered explainable AI systems translating clinical natural language queries into optimized SQL via graph-based reasoning, increasing analytics efficiency by 65% and improving fraud/risk detection accuracy by 20% using Azure ML and AWS SageMaker.

•Delivered HIPAA-compliant virtual care AI solutions using TensorFlow, PyTorch, and Hugging Face Transformers, enhancing patient monitoring outcomes by 35%.

•Built LLM orchestration pipelines with LangChain, LlamaIndex, Semantic Kernel, and CrewAI for query decomposition, structured-output SQL translation, and multi-LLM coordination, boosting analytical efficiency by 65%.

•Designed and optimized RESTful and gRPC APIs using Java, Spring Boot, and Hibernate/JPA, ensuring low-latency access to large-scale relational and analytical datasets.

•Implemented Kafka-based event streaming pipelines in Java for real-time ingestion and processing of operational and clinical data, improving throughput by 45%.

•Developed concurrent and multithreaded Java services to support high-volume transaction processing with strict SLA requirements.Implemented real-time data streaming in React SPAs using WebSockets and Redux, improving clinical alert delivery speed by 25%.

•Led MLOps and containerized deployments with Docker, Kubernetes, and CI/CD pipelines (GitHub Actions), building APIs for cross-organizational ML/LLM communication, implementing monitoring, alerting, and automated model drift mitigation, maintaining 92% service reliability.

Bruker Sep 2021 - Oct 2023

Senior AI Engineer/Full Stack Engineer Massachusetts, USA

•Developed AI-powered web applications and LLM-based copilots using OpenAI, Anthropic, Cohere, Gemini, and backend APIs to automate clinical analytics and structured decision workflows.

•Built production ML workflows for fraud and risk detection using AKS APIs and AWS Step Functions, combining Python inference layers with Scala/Spark batch scoring to handle over 65K daily predictions with 99.9% uptime.

•Designed LLM-powered evaluation and RAG pipelines with LangChain, LlamaIndex, Weaviate, and Pinecone to benchmark document retrieval and summarization across healthcare and microscopy datasets.

•Deployed GenAI-enhanced PyTorch and Scikit-learn models into production pipelines, improving pharmaceutical returns performance by 25% and data accuracy by 20% while ensuring operational reliability.

•Built high-performance Java Spring Boot microservices for clinical analytics and workflow orchestration, supporting secure API communication across distributed healthcare systems.

•Designed NLP pipelines integrating Hugging Face Transformers with PySpark, reducing patient document processing time by 40% and increasing classification accuracy by 20%.

•Maintained integrations with Salesforce, Asana, and Google Sheets for automated task management, reporting, and operational QA workflows.

•Implemented MLOps frameworks using Airflow, Dagster, and AWS CI/CD tools (CloudFormation, Lambda, SageMaker), reducing model drift by 30% and ensuring regulatory compliance.

•Built advanced NLP models for clinical document analysis and health-related inquiries, improving automated response accuracy by 25%.

•Engineered enterprise streaming architectures with Go microservices, Kafka, and PostgreSQL, supporting high-throughput concurrent event processing (over 50%) and fault-tolerant operations.

•Developed MCP-enabled AI copilots for surgical analytics platforms, enabling structured tool use and repeatable reasoning within AWS environments.

•Built backend services using Python, Node.js, and C#, integrating robust APIs and scheduling for repeatable automation tasks.

•Developed multimodal AI pipelines for computer vision and document understanding using TensorFlow, Keras, ResNet50, and CNNs, deploying via APIs with automated orchestration, improving search precision and reducing manual oversight by 35%.

Phlux Technologies Feb 2019 - Sep 2021

Machine Learning Research Scientist/senior full stack engineer Pittsburgh, Pennsylvania

•Built evaluation frameworks for RAG-enabled LLM systems using Azure OpenAI, FAISS, and Pinecone to benchmark document understanding and portfolio analysis speed.

•Developed a conversational AI interface for financial analysis using Django, reducing manual reporting by 50% and increasing research turnaround speed by 40%.

•Applied ML and NLP toolkits to analyze sentiment, generating data-driven insights that increased patient satisfaction metrics by 20%.

•Integrated FAISS and Pinecone into RAG pipelines, achieving a threefold improvement in hybrid search performance for real-time financial information retrieval.

•Operationalized ML models with Azure ML and MLflow, leveraging Redis caching and Kubeflow pipelines to ensure repeatable, reliable, and low-latency model updates.

•Orchestrated ML CI/CD pipelines using Kubernetes, MLflow, and NVIDIA Triton, reducing model deployment timeframes by 30%.

•Engineered high-throughput data ingestion pipelines processing over 10TB per day with sub-second latency, integrating Claude and GPT-4 while maintaining fault-tolerant distributed operations.

•Designed and deployed multi-agent market simulation frameworks with RESTful APIs, SQLite microservices, and real-time state synchronization, enabling scalable, concurrent enterprise simulations.

•Developed AI-powered analytical and cybersecurity tools with LLMs, NLP frameworks, and graph databases, supporting proactive threat detection, RAG-driven intelligence, and interactive visualizations for ISR/EO/IR workflows.

•Built web interfaces and AI query modules using Angular, React, and Django, implementing feedback-driven optimization loops that reduced manual document handling by 30% and enhanced decision-making reliability.

Dormakaba Jan 2017 - Feb 2019

Senior Full-Stack Developer Lexington,Kentucky

•Developed full-stack automation tools with Python, Node.js, C#, and React to replace repetitive manual operational workflows.

•Built declarative single-page applications with React/Vue.js and Redux, improving re-render efficiency by 30% and enhancing user experience for high-concurrency systems.

•Engineered full-stack applications with Go, GraphQL, Node.js, and Python, enhancing API security, database integration, and data throughput, reducing query latency by 8ms across multiple services.

•Designed and deployed robust RESTful APIs with OAuth2/JWT authentication and Spring Boot, enabling secure data exchange between trading and backend systems.

•Developed backend services for real-time streaming, handling multiple camera feeds, WebSockets, and REST APIs to support high-frequency data ingestion and analytics pipelines.

•Implemented SQL and NoSQL databases to support microservices, analytics, and reporting, integrating with scalable backend architectures suitable for AI/ML pipelines.

•Led development of user-centric applications including inventory tracking and operational dashboards with React, Next.js, and TypeScript, reducing manual effort by 40% and enabling structured data access for downstream automation.

Education

Northern Kentucky University 2017–2022

Doctorate, Artificial Intelligence

The University of Sheffield 2013–2015

Master’s Degree, Computer Science

The University of Sheffield 2011–2013

Bachelor of Science, Computer Science

Cochise College 2008–2011

Associate’s Degree, Mathematics

Skills

Artificial Intelligence & Machine Learning: TensorFlow, PyTorch, Hugging Face Transformers, LlamaIndex, Semantic Kernel, CrewAI, Scikit-learn, NLP, Generative AI (GenAI), Retrieval-Augmented Generation (RAG), LangChain, Computer Vision, BERT, ONNX Runtime, Triton Inference Server, AutoML, TPOT, Benchmarking Frameworks, TTFT, SuperAnnotate, MLflow

Agentic & Multi-Agent Systems: Agentic AI, Multi-Agent Workflow Design, Autonomous Data Orchestration, Agentic Observability Systems, LLM Feedback Loops, Self-Healing Pipelines, AutoGen

MLOps & Model Management: Databricks, PySpark, MLflow, Model Serving, Monitoring & Observability (Prometheus, Grafana, ELK), Logging & Alerting Systems, CI/CD for ML, Infrastructure as Code, Cloud Platforms (AWS, GCP, Azure), Serverless Architectures, API Gateways

Cloud & DevOps: AWS (SageMaker, Bedrock, ECS, Lambda, CloudWatch), GCP (Vertex AI, Cloud Run, BigQuery), Azure (Cognitive Services, Functions), Docker, Kubernetes, Helm, GitHub Actions, GitOps, Cloud-Native Deployment, Performance Optimization

Software Engineering & Programming: Python, Go, C++, Node.js, TypeScript, Flask, FastAPI, Express, RESTful APIs, GraphQL, High-Concurrency Backends, Distributed Systems, Microservices Architecture

Data Engineering & Storage: ETL Pipelines, Data Warehousing, Schema Design, PostgreSQL, MongoDB, Redis, BigQuery, Vector Databases (Weaviate, Milvus), ElasticSearch, OpenSearch, Hybrid Search, Keyword Search, Data Lineage, Data Quality Pipelines, Secure Data Handling & Encryption

Frontend Engineering: React, Redux, Angular, Next.js, TypeScript, Material UI, Tailwind CSS, SPA Development, Context API, Data-Driven Dashboards

Security & Compliance: HIPAA Compliance, Identity & Access Management, Secure API Development, Vulnerability Assessment, Penetration Testing, Cloud Security Controls, Encryption Standards

Key Projects and Achievements

AI-Driven Medical Intake Assistant: Built multi-stage reasoning framework with Django, GPT-4o, and LangChain for agent-based workflow management, automating eligibility verification and structured data extraction; increased clinical throughput by 50%.

Real-Time Patient Monitoring: Developed React/Redux interfaces integrating TensorFlow CV models on GCP Vertex AI with streaming pipelines and WebSockets; improved alert cycle efficiency by 20% for 300+ simulated patients, maintaining HIPAA compliance.

Natural Language to SQL Query Engine: Used AutoGen, LangGraph, and LangChain to convert natural language into optimized SQL queries; improved graph traversal, multi-table joins, and context-aware query expansion, resulting in a 1.7-fold increase in analytical performance.

Multi-Agent Risk Assessment System: Leveraged Pydantic AI and CrewAI on Azure ML for coordinating AI agents across terabyte-scale vector searches in PostgreSQL and BigQuery; reduced review time by 45%.

Prompt Testing & Benchmarking: Created zero/few/system-shot prompt suites and automated scoring frameworks to optimize model outputs across Azure OpenAI and Anthropic.

RAG-Based Financial Document Analysis: Built pipelines with Azure OpenAI, LlamaIndex, FAISS, vLLM, mixed-precision quantization, and GPU batching; increased portfolio evaluation efficiency by 40% and reduced inference time by a factor of 12.

Cybersecurity Threat Intelligence Platform: Developed LangChain and Django/React-based RAG pipelines on graph databases, enabling proactive threat detection.

Distributed Market Simulation Framework: Designed multi-agent simulation with REST APIs, SQLite microservices, Kafka streaming, Docker, and Terraform; processed 2M+ real-time events daily in hybrid cloud environments.

High-Performance LLM Inference Framework: Leveraged vLLM, Flash Attention, quantization, and NVIDIA Triton in Kubernetes clusters to optimize LLM inference efficiency.

Certifications

Deep Learning for Intelligent Video Analytics

Convolutional Neural Networks

Neural Networks and Deep Learning

Structuring Machine Learning Projects

Introduction to Responsible AI

Introduction to Large Language Models

Introduction to Generative AI

Certified Scrum Product Owner (CSPO)

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