🧠 ML-first developer and platform architect building secure, production-grade systems for automation, inference, and edge intelligence.

Developer and architect with 20+ years in IT and a focus on platform engineering, automation, and data-driven infrastructure.
Currently specializing in deep learning, edge AI, and sensor-driven inference pipelines after years of designing secure backend systems and operational frameworks.

• Designing full-stack machine learning pipelines with PyTorch, MLflow, and FastAPI
• Orchestrating data and model workflows using Prefect, Vault, Postgres, and MinIO
• Developing real-world vision systems with CNN-based classification and object detection
• Building edge AI solutions on ESP32-class devices with multi-sensor fusion and local inference
• Creating interactive frontends with Next.js and D3.js for control interfaces and data visualization
• Advancing toward autonomous systems, embedded ML, and robotic decision-making

• infra-service-backend
  Clean, deployable service templates for ML pipelines — includes Prefect, MLflow, Vault, Postgres, and secure orchestration infrastructure.

• data-orchestration-prefect-etl
  Real-world ETL pipelines using Prefect with Vault, MinIO, and Postgres — designed for API and scrape-based automation.

• classical-ml-scikit-learn
  Structured pipelines using scikit-learn — covering regression, classification, clustering, dimensionality reduction, and model evaluation.

• deep-learning-pytorch
  Hands-on deep learning with PyTorch — CNNs, classification, modular training, transfer learning, and experiment tracking with MLflow.

• math-machine-learning
  Foundational math notebooks for ML — covering calculus, linear algebra, and probability/statistics to support algorithmic understanding.

• Director of Infrastructure
• Principal Developer and System Architect
• Technical Lead and Team Manager
• Lead on Internal IT Architecture, Data Operations, and Platform Automation
• Backend Developer for Scripting, Monitoring, Deployment, and Vulnerability Remediation
• Data Engineering Lead for Secure Ingestion, Schema Design, and Delivery Pipelines
• ML Pipeline Developer Focused on Platform Integration and Reproducibility
• Infrastructure and Data Observability Systems Designer
• Product Owner for Internal Integrations and Third-Party Tooling
• Documentation Author for SOPs and Operational Standards
• Peer Reviewer for Infrastructure, Automation, and CI/CD Across Cross-Functional Projects

I build scalable systems by applying broad technical experience and a deep grasp of the ML lifecycle, from ingestion to deployment.

• FastAPI service development and API integration
• Task queuing and background jobs with Celery and Redis
• Service orchestration and messaging between microservices

• OAuth2 / OpenID Connect (OIDC)
• MSAL (Microsoft Authentication Library for Azure AD / Entra ID)
• SAML 2.0 (enterprise SSO and identity federation)
• JWT (JSON Web Tokens) with custom claims
• TOTP (Time-based One-Time Passwords) for MFA
• Secure session management (cookie- and token-based)
• Token lifecycle handling (refresh, expiration, revocation)
• RBAC / ABAC (role- and attribute-based access control)
• User and machine-to-machine authentication

• Orchestrated ETL from authenticated APIs, login-based frontends, and web-scraped data
• Intermediate processing with Pandas for join logic, cleanup, and schema enforcement
• Scalable pipelines using Apache Spark and PySpark for distributed data processing
• Workflow orchestration with Prefect, including retries, triggers, and state tracking
• Postgres-based Kimball and OBT modeling for ML-ready pipelines
• File-based I/O using Parquet, JSONL, and CSV
• Object storage with MinIO (S3-compatible) for data lakes and model artifacts
• Schema validation and enforcement with pydantic for API and pipeline input consistency
• Early-stage work with Kafka and MQTT for real-time and event-driven ingestion
• Data versioning and lineage tracking (planned: DVC, Delta Lake for Databricks)

• Model versioning and experiment tracking with MLflow
• CI/CD deployment of models with Prefect and FastAPI
• Containerization with Docker, orchestration with Kubernetes (exploratory)
• Secure storage and service credentials with Vault
• Artifact pipelines, scheduled jobs, and failure-aware execution

• Dashboard UIs and control panels using React and D3.js
• Data apps and visual pipelines using Streamlit and Plotly
• JSON/state-driven visual updates and user interaction modeling

• Systems & endpoint management (Windows, Linux, Active Directory, Group Policy, Intune)
• Cloud infrastructure & IaC (Terraform, AWS, Azure)
• Hybrid identity and access management (Azure AD, AD Connect, conditional access policies)
• Networking (VPN, VLAN, routing)
• Security & DevSecOps (RBAC, secrets management, tenant policy enforcement)
• Backup & disaster recovery
• O365 / Exchange administration
• Observability and logging (Grafana, Loki, Promtail, custom logging systems)
• Ticketing & PSA workflows (internal tooling, vendor escalation pipelines)

My current work is focused on building systems that are not just automated, but unified and predictive, with centralized data models, consistent pipelines, and actionable insights.

• Client-aware – surfacing contextual data across endpoints, alerts, tickets, usage, time, and human responses
• Self-auditing – producing consistent, exportable records of service coverage and gaps
• Proactive – identifying patterns and triggers before reactive support is needed
• Unified – drawing from a single dataset to enable operational clarity across teams
• Compliant by design – using internally developed automation for lifecycle events like deprovisioning, offboarding, and reporting

• Observability pipelines that collect, transform, and interpret data from diverse sources
• ETL and orchestration systems with feedback loops and failure awareness
• Feature engineering techniques that unify and enhance fragmented data sets
• ML workflows that train models to make proactive, scalable predictions
• Reproducible ML pipelines designed for deployment and long-term support

• Leverage systems and monitoring expertise to deliver resilient, production-grade ML platforms
• Transition from scheduled automation to real-time inference and event-driven architectures
• Advance into edge AI and robotics, applying local inference and threshold-based logic for autonomous, scalable decision-making

• GitHub: @gabemcwilliams
• LinkedIn: linkedin.com/in/gabemcwilliams
• Email: gabe@gabemcwilliams.info