Platform/SRE Engineer Data Science (M.S.) Python/SQL ML Systems

JOSHUA WOLFE

Senior MLOps / ML Platform Engineer • Data Scientist • Multi-Cloud Reliability Leader

Cedartown, GA • Remote (U.S.) 470-***-**** ************@******.*** LinkedIn GitHub Portfolio Live Demo

SUMMARY

MLOps / platform engineering leader with 16+ years supporting SaaS production systems and 10+ years of technical leadership (mentorship, incident command, delivery accountability). 5+ years building cloud infrastructure across AWS, Azure, and Google Cloud. M.S. in Data Science with hands-on ML across NLP, predictive modeling, and deep learning

(TensorFlow/Keras LSTM), plus data pipelines/ETL. Strength in translating telemetry, incidents, and KPIs into durable improvements in reliability, change safety, and developer productivity. AI / DATA SCIENCE & MLOPS HIGHLIGHTS

• Built and deployed an adaptive AI simulation engine with FastAPI, WebSockets, SQLite, Docker, and TensorFlow/Keras

(live demo).

• Designed end-to-end system flows from data capture feature engineering training/inference deployment & observability.

• Implemented NLP workflows with Natural Language Toolkits (e.g., NLTK) for tokenization, text features, and sentiment- style pipelines.

• Experienced with supervised learning (regression, decision trees, random forest, gradient boosting) and clustering

(hierarchical, k-NN); comfortable selecting models, evaluating performance, and optimizing KPIs.

• Cloud data patterns: streaming ingestion (AWS Kinesis), distributed processing concepts (MapReduce/EMR-style), and ETL orchestration (Airflow conceptually and operational support contexts). TECHNICAL SKILLS

ML / AI: Python, scikit-learn, TensorFlow/Keras (LSTM), NLTK, feature engineering, model evaluation, regression, trees/forests, gradient boosting, clustering (hierarchical), k-NN Data Engineering: SQL, pandas, NumPy, ETL/pipelines, streaming concepts (Kinesis), distributed processing concepts

(MapReduce), Redshift

MLOps / Platform: Docker, Kubernetes/EKS, CI/CD (GitHub Actions, Jenkins), observability (CloudWatch, Datadog, Prometheus, Grafana), SLO/MTTR, incident response & RCA Cloud / IaC: AWS (multi-account foundations, IAM, networking), Azure, Google Cloud, Terraform, CloudFormation, ECS/Fargate

Systems: Linux/RHEL, Bash, PowerShell, YAML/JSON

PROFESSIONAL EXPERIENCE

OLLION (formerly 2nd Watch) — Senior Cloud Engineer / Engineering Supervisor, Platform & Reliability Remote • Mar 2022 – Apr 2026

• Led and scaled a global team of up to 16 engineers: hiring, onboarding, mentorship, and performance alignment to operational KPIs and delivery outcomes.

• Defined and executed platform strategy for cloud-native systems; delivered scalable AWS + Kubernetes (EKS) environments supporting production workloads.

• Owned reliability outcomes as Incident Commander; led high-severity response, postmortems/RCA, and continuous improvement to reduce recurrence and improve MTTR.

• Standardized Infrastructure as Code (Terraform/CloudFormation) for repeatable, secure dev/stage/prod deployments, reduced configuration drift and operational toil.

• Established CI/CD and change-safety guardrails (rollbacks, release readiness, change management) to improve deployment reliability.

• Built observability standards across CloudWatch, Datadog, Prometheus, and Grafana; improved alert signal quality and expanded synthetic monitoring.

• Automated database lifecycle operations (deployments, backups, patching) increase repeatability and reduce manual risk.

• Coordinated cross-regional enablement and workflow standardization across Airflow, Kubernetes, and AWS ecosystems.

HUNTINGTON INGALLS INDUSTRIES — Database Administrator / Systems Analyst Aug 2015 – Mar 2022

• Delivered Tier 3 enterprise support in regulated environments; resolve complex issues and ensure mission continuity.

• Administered IAM systems (Active Directory OU design, CAC authentication, YubiKey MFA) and enforced secure access practices.

• Supported infrastructure across VMware, Kubernetes platforms, and Windows/Linux systems, improved reliability via standardization, automation, and documentation.

• Automated provisioning and administrative workflows with PowerShell; improved turnaround for VM creation and configuration.

• Built internal dashboards/knowledge base with FAQs and runbooks to improve support efficiency and accelerate onboarding.

SELECTED AI / DATA PROJECTS

Labyrinth of Tartarus — Live Adaptive AI Simulation Engine Tech: FastAPI, WebSockets, SQLite, Python, Docker, TensorFlow/Keras Links: Live Demo GitHub

• Built and deployed a live AI-driven system with real-time telemetry ingestion, adaptive behavior logic, and persistent storage.

• Architected separation between data capture, preprocessing, training/inference, analytics, and runtime services.

• Demonstrates end-to-end ML engineering beyond notebooks: backend services, model-ready telemetry, containerized deployment, and live demo.

Data Science & Analytics Portfolio

Links: gift3dmyndz.github.io/Data-Science-Projects

• Applied ML projects including LSTM time-series forecasting, customer segmentation/clustering, NLP sentiment analysis, and automated ETL pipelines.

• KPI dashboards and analytical narratives emphasizing data quality, feature engineering, evaluation, and business- readable insights.

EDUCATION

Master of Science (M.S.), Data Science — University of Phoenix (Completed Apr 2026) Bachelor of Science (B.S.), Information Technology / MIS — University of Phoenix (Completed 2021) HONORS

• University of Phoenix President’s List — Jan–Jun 2025

• University of Phoenix Dean’s List — 2018

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