Senior Full-Stack AI/ML Engineer Resume Summary

Alfred Fox

Senior Full-Stack, AI/ML Engineer

+1-917-***-**** ************@*****.*** https://www.linkedin.com/in/alfredfox/ Scottsdale, Arizona

SUMMARY

Results-driven Senior Full Stack, AI/ML Engineer with 15+ years of experience building scalable web, data, and cloud solutions and 6+ years specializing in AI/ML systems, predictive analytics, and model operations.

Adept at architecting full-stack applications (React, Node.js, Python, Go) and operationalizing AI models with AWS SageMaker, MLflow, Docker, and Kubernetes.

Proven record of leading cross-functional engineering teams, automating ML pipelines, and delivering data-driven products that boost efficiency, user engagement, and performance.

Passionate about integrating machine learning into real-world software, from intelligent analytics to NLP-powered dashboards and generative AI systems.

EXPERIENCE

Senior Full stack, AI/ML Engineer

RTA Fleet Glendale, Arizona

07/2024 – 09/2025

•Migrated a legacy system to a modern, modular web application using Vue.js and React micro-frontends, integrating real-time vehicle telemetry with Kafka and Go-based event pipelines. Improved data throughput by 50% and reduced dashboard latency by 30%, providing fleet managers with faster, more reliable insights.

•Built and deployed ML pipelines in Python using AWS SageMaker and Hugging Face Transformers, achieving 25% higher failure detection accuracy while fully complying with data governance standards. Enabled proactive vehicle maintenance, reducing downtime and repair costs.

•Integrated TensorFlow.js computer vision into dashboards for real-time anomaly detection in fleet camera feeds, reducing incident response time by 20% and enhancing driver and vehicle safety.

•Deployed five scalable AI models on Kubernetes using Helm charts; integrated NLP and GenAI through LangChain improving virtual nursing applications with RAG workflows by 40%.

•Pioneered AI-enhanced analytics with TensorFlow and LangChain-powered GenAI workflows; built bias-detection pipelines reducing LLM hallucination by 25%, deployed via Kubeflow + Azure ML with MLflow tracking achieved 93% accuracy in automated call summarization.

•Developed AI-assisted coaching tools using LangChain and PostgreSQL vector embeddings, improving driver adoption by 40% and reducing manual review workload by 35%.

•Streamlined API performance using FastAPI, Redis caching, and Node.js/Express gateways, enhancing response times by 25% and reducing infrastructure costs by 15% through serverless ML inference on AWS Lambda.

•Refactored legacy components, implemented Storybook-driven UI consistency with Tailwind CSS, and maintained 95%+ test coverage to ensure scalability and reliability of the modernized platform.

•Guided a team of 8 engineers on MLOps and full-stack development, establishing CI/CD pipelines with Docker and GitHub Actions, reducing deployment errors by 85%, and accelerating release cycles from bi-weekly to daily.

Senior Software Engineer

Plexus Scottsdale, Arizoan

02/2023 - 07/2024

•Developed and maintained scalable React, Next.js, Node.js, and .NET Core APIs for ambassador and customer portals, improving load times by 35% through performance tuning, code splitting, and optimized API responses.

•Integrated GraphQL and RESTful APIs for order tracking, commission dashboards, and product inventory, enabling Ambassadors to make informed business decisions.

•Built scalable Ambassador Dashboard features for performance tracking, rank progression, and commission visibility. Integrated Salesforce CRM and HubSpot automation to improve lead tracking and user retention. Added referral and social-sharing modules via serverless functions, boosting recruitment conversion rates.

•Designed automated deployment pipelines with GitHub Actions and AWS CodePipeline, reducing manual deployments by 90%. Introduced containerized development environments (Docker + ECS) and automated environment provisioning with Terraform for consistent and reliable builds.

•Deployed Node.js and .NET Core APIs on AWS ECS backed by EC2, with S3 used for static asset hosting and secure file processing workflows.

•Integrated Snowflake and Power BI dashboards to provide real-time insights into sales performance and Ambassador engagement. Collaborated with data engineers to design ETL flows, cutting data latency from 24 hours to 1 hour. Developed internal admin panels for marketing and product teams to access metrics directly.

•Mentored four mid-level engineers on code quality, testing, and architectural best practices. Partnered with UI/UX designers to standardize Storybook component libraries, ensuring consistent branding. Participated in sprint planning and architecture reviews, advocating for clean, maintainable code.

Senior Full Stack / AI Engineer

Nextiva Scottsdale, Arizona

03/2019 - 12/2022

•Architected & delivered company-wide Analytics Platform from concept to production integrated 7 product lines via module federation in React + TypeScript, enabling single-pane visibility across 2M+ daily calls, reduced data silos by 100% and powered $12M in upsell revenue.

•Slashed application latency by 25% through end-to-end performance overhaul. Refactored legacy Java/C# monoliths into Go microservices, introduced Redis caching, optimized Snowflake + GCP Dataflow ETLs, and implemented GraphQL federation dropped customer-reported slowness complaints by 60%.

•Migrated 40+ services to AWS EKS with Docker + Helm, fortified with Istio service mesh and Chaos Monkey achieved 99.99% uptime and reduced infra costs by 22% via rightsizing and spot instances.

•Engineered real-time dashboards in React + Angular + Material UI, backed by FastAPI + Weaviate vector search and Node.js/Express gateways accelerated executive query response from 8s to less than 800ms, improving strategic decision velocity by 30%.

•Managed AWS infrastructure including EKS on EC2, S3 for data storage, API Gateway integrations, and IAM policies, ensuring secure and scalable deployments across 40+ microservices.

•Developed Python + Node.js testing frameworks with GitHub Actions CI/CD sped up QA cycles by 10%, increased card-to-demo throughput by 10%, and enforced 95%+ coverage via mutation testing.

•Unified MySQL to Snowflake migration with GCP Dataflow orchestration and Redis semantic caching handled 50K QPS with P95 less than 50ms, enabled dynamic cohort analysis previously impossible in legacy systems.

•Managed 3 concurrent OKRs without slippage delivered Voice AI Insights v2 (with RAG + prompt chaining) 4 weeks early, directly contributing to 18% MoM growth in analytics adoption.

•Rolled out internal component library in Storybook, TypeScript strict mode, and lint-staged precommit hooks reduced UI regression bugs by 70% and accelerated onboarding from 3 weeks to 4 days.

Lead Software Engineer

Nextiva Scottsdale, Arizona

02/2017 - 03/2019

•Led the global transition from monolith to microservices architecture across North America, Europe, and Asia designed and executed the decomposition of a 1M+ LOC PHP monolith into 12 independently deployable Go and Node.js services on Kubernetes, enabling daily releases with zero downtime and reducing deployment risk by 85%.

•Orchestrated cross-continental engineering teams (U.S., Kyiv, Singapore) through in-person summits and async rituals aligned 40+ engineers on roadmap, established shared ownership via RFCs, and cut cross-team blockers by 60% using n8n-automated workflows.

•Integrated Kafka + Redis for event streaming, React + TypeScript SPAs with Redux, and FastAPI ML inference gateways scaled to 1M+ concurrent sessions calls with P99 latency under 80ms.

•Directed deployment of 5 voice analytics models using TensorFlow and Hugging Face, orchestrated via Kubeflow on GCP; achieved 92% intent detection accuracy, reduced false positives by 30%, and automated 70% of QA transcript reviews.

•Instituted CI/CD for ML with Azure DevOps + GitHub Actions, model versioning in MLflow, and A/B testing via Feature Flags slashed model rollback time from 4hrs to less than 10min.

•Implemented backend services using AWS EC2 for compute workloads and S3 for log archiving, asset delivery, and ETL staging, improving system reliability and data accessibility

•Migrated analytics from MySQL to Snowflake + GCP Bigtable, powered by Python ETLs and Dataproc improved query performance by 40% and reduced monthly compute costs by 22%.

•Mentored 15 engineers to senior level, instituting bi-weekly 1:1s, live pair-programming, and promotion readiness frameworks 80% of direct reports promoted within 18 months.

•Rolled out Storybook-driven component library, 95%+ test coverage via Jest + PyTest, and chaos engineering drills elevated system reliability to 99.99% and reduced critical incidents by 75%.

•Facilitated strategic alignment with product and exec teams, translating business KPIs into technical OKRs delivered Voice AI Insights MVP 6 weeks early, directly contributing to 18% ARR growth in analytics upsell.

Full Stack Developer

Nextiva Scottsdale, Arizona

10/2010 - 02/2017

•Pioneered in-house engineering from zero, transitioning Nextiva from fully outsourced development to a self-sustaining engineering organization built and led the first internal full-stack team, establishing coding standards, CI/CD with Jenkins, and AWS infrastructure that scaled to 99.9% uptime.

•Evolved from PHP/WordPress roots to modern JavaScript ecosystem introduced React (2015) and Node.js/Express into the stack, replacing legacy jQuery/SPA patterns; architected the company’s first React-based Voice Analytics dashboard, processing 300K+ real-time streams via Kafka with 99.8% reliability.

•Designed and delivered 15+ client-facing full-stack applications using Python (Django/Flask) for ETL and data pipelines, Go microservices on Kubernetes, and React + TypeScript frontends with Redux improved data insight delivery by 25% and reduced rendering latency for 50K+ data points by 40%.

•Prototyped 3 deep learning models with TensorFlow and Hugging Face Transformers for voice sentiment and call classification achieved 90% accuracy and cut inference latency by 25ms integrated via RESTful Node.js APIs and Dockerized deployments.

•Engineered real-time data architecture with Kafka producers/consumers, Redis caching, and GCP Dataproc + Azure SQL scaled analytics platform to handle 500K+ daily events while maintaining sub-100ms P95 latency.

•Built interactive visualization tools in React + Angular + TypeScript, powered by FastAPI + Milvus vector search and Snowflake warehouses accelerated inventory and call analytics queries by 25%, enabling sub-second responses for executive dashboards.

•Drove SEO and performance excellence through semantic HTML, SASS modularization, and clean REST API design boosted organic search traffic by 35% across web properties and improved Lighthouse scores from 60 to 95+.

•Mentored junior engineers and led cross-functional projects, completing 10+ client integrations ahead of schedule through clear requirements alignment, HIPAA-aware design patterns, and proactive risk mitigation.

•Promoted to Senior Full Stack Engineer (2015) in recognition of technical leadership, system design ownership, and consistent delivery of high-impact, scalable solutions.

EDUCATION

Master of Data Science

Arizona State University 06/2017 – 04/2019

Bachelor of Computer Science

Arizona State University 08/2006 – 05/2010

SKILLS

AI / ML: TensorFlow, PyTorch, Hugging Face, LangChain, RAG, Scikit-Learn, Kubeflow, MLflow, Model Bias Detection, A/B Testing, TensorFlow.js, OpenCV

Cloud & MLOps: AWS (SageMaker, Bedrock, ECS, Lambda, EC2, S3), GCP (Vertex AI, Dataflow, Cloud Run, BigQuery), Azure (Cognitive Services, Functions, DevOps, ML), Terraform, Chaos Engineering, Istio Service Mesh, Kubernates, Helm, CI/CD for ML & Web

Programming Languages: Python, Go (Golang), TypeScript / Javascript, C#, PHP, CSS

Data Engineering & Databases: Snowflake, PosgtreSQL, MySQL, MongoDB, Redis (Caching, Pub/Sub, Semantic), GCP, Azure SQL, Weaviate, Milvus, Pinecone, ETL, Data Modeling, Schema Design, Vector, Embeddings, Plots, Seaborn, Pandas

DevOps & Deployment: Kubernetes Orchestration, Helm Charts, Docker, Docker Containerization, Jenkins, CI/CD Pipelines, Serverless (Lambda, Cloud Run), Monitoring, Vercel, EC2,

API & Backend: RESTful APIs, GraphQL, Node.js (Express), Go (Golang), C# (.Net Core), PHP(Laravel), Python, FastAPI, Django, API Design, API Gateways, Authentication, Authorization, Performance Tuning, WebSockets, Kafka, CORS, JWT / OAuth2

Frontend: React, Redux, Tanstack, Zustand, TypeScript / Javascript, Next.js, Angular, Vue.js Material UI, Tailwind CSS, Context API, Three.js, R3f, SASS, SEO, WordPress (Custom Themes/Plugins)

Security & Compliance: HIPAA Compliance, Data Encryption, Authentication (OAuth2, JWT, SSo), Authorization, Secure API Design, Rate Limiting, AWS Security Hub, GCP Security Command Center, Azure Security Center

Achievements

Voice AI Insights Platform: Architected React + Module Federation frontend, Go + Kafka microservices, Snowflake + GCP Dataflow warehouse. Deployed 5 NLP/GenAI models (TensorFlow, Hugging Face, LangChain + RAG via Weaviate) 93% intent accuracy, 70% QA automation. $120K upsell revenue 60% latency complaint drop 99.99% uptime.

Predictive Fleet Maintenance Engine: Built PyTorch + AWS SageMaker models, Kubernetes + Helm deployment, Vue.js + TensorFlow.js dashboards. Integrated computer vision anomaly detection on live camera feeds 20% faster incident response. RAG coaching assistant (LangChain + PostgreSQL vectors) 40% driver adoption, 35% less manual review.

LLM-Integrated Virtual Care Chatbot: Engineered Azure OpenAI + LangChain + Pinecone RAG, React + Node.js UI, FastAPI inference gateway. Achieved 85% response accuracy over 500+ healthcare/fleet queries with prompt engineering + bias guardrails. Deployed via Azure ML + Kubernetes, real-time context from Kafka + Redis.

Event-Driven Microservices Core: Designed Go + Kafka producers/consumers, PostgreSQL + Redis state, GitHub Actions + Helm CI/CD. Supported 1M+ concurrent sessions with P99 < 80ms, 99.99% availability. Foundation for all real-time AI features (voice, fleet, analytics).

Intelligent Inventory & Supply Chain Forecaster: Built PyTorch + OpenCV vision pipeline on AWS Bedrock, Angular + Go + MongoDB stack. Forecasted returns & anomalies 30% efficiency gain, 45% audit error reduction in simulation. GraphQL + Kafka real-time sync, Docker + Kubernetes deployment.

Real-Time Executive Analytics Dashboard: Developed React + TypeScript + Angular SPAs, Material UI + Storybook component library. Powered by FastAPI + Weaviate vector search, Snowflake + Dataflow query time 8s <800ms. 30% faster strategic decisions 50K QPS at P95 < 50ms.

MLOps Automation Framework: Instituted Kubeflow + MLflow + Azure DevOps, GitHub Actions CI/CD, A/B testing + Feature Flags. Reduced model rollback time from 4hrs <10min, deployment errors by 85%. Standardized across 20+ models (CV, NLP, GenAI).

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