Owen User
@owenuser4
AI and full-stack software engineer building production platforms for trustworthy ML, voice agents, and real-time network anomaly detection.
What I'm looking for
I’m an AI and full-stack software engineer with 8+ years of experience building production software platforms spanning AI applications, ML platforms, network intelligence, cloud infrastructure, and research-driven computer vision. At Comcast, I built AI-enabled internal engineering and security applications around Project GuardRail, including intake-to-approval workflows, audit trails, and risk assessment using React, Next.js, FastAPI, GraphQL, and PostgreSQL/Redis.
I also integrated Azure OpenAI GPT-4o for summarization, classification, and structured extraction with JSON schema validation and fallback handling, and I built an internal AI voice agent using Twilio Voice, Deepgram STT, Azure Speech TTS, FastAPI, WebSockets, and OpenSearch for retrieval-grounded, conversational answers and escalation flows. Previously, I led AMP’s ML platform work, orchestrating model training and batch inference with Airflow/Spark/Kafka and tracking experiments with MLflow—backed by strong observability, RBAC, PII-safe logging, and CI/CD ownership.
Experience
Work history, roles, and key accomplishments
Built AI-enabled internal engineering and security applications for Project GuardRail using Java, Python, Go, React/Next.js, FastAPI, GraphQL, and PostgreSQL/Redis. Developed GraphQL/React dashboards, Azure OpenAI GPT-4o workflows, and an OpenSearch-backed AI voice agent with Twilio Voice and Deepgram.
Led development of AMP, Comcast’s internal ML application platform, delivering ML workflow services, REST APIs, and React interfaces. Built Airflow and Spark pipelines for training, batch inference, and evaluation; integrated MLflow for experiment/model tracking and Kafka for ingesting operational signals.
Computer Vision Research Assistant
Dr. Richard Souvenir Research Group
Dec 2016 - Feb 2020 (3 years 2 months)
Built computer vision and ML research pipelines to study human driving behavior and the effects of low autonomous-vehicle penetration in mixed traffic. Implemented vehicle detection/tracking and feature extraction workflows to evaluate how different adoption levels impact traffic flow, safety behavior, and congestion.
Developed a real-time network anomaly detection ML application using IP SLA probe data to identify abnormal packet loss between data centers and Cloud-RAN. Created scalable anomaly-detection pipelines with time-series feature engineering and exposed alerts/metrics via APIs and dashboards for network operations teams.
Education
Degrees, certifications, and relevant coursework
Temple University
Bachelor of Science, Mathematics and Computer Science
2015 - 2018
Earned a B.S. in Mathematics and Computer Science with a computer science specialization within Cognitive Science.
Tech stack
Software and tools used professionally
Apache Spark
GitHub
Kubernetes
Cloudflare
Jenkins
Jupyter
NumPy
Pandas
PostgreSQL
Gmail
Node.js
Next.js
OpenCV
Redis
React
JavaScript
Python
HTML5
Java
CSS 3
JSON
TensorFlow
MLflow
scikit-learn
Keras
Kafka
FastAPI
Grafana
Prometheus
Datadog
GraphQL
gRPC
Elasticsearch
OpenSearch
Deepgram
TypeScript
Docker
Twilio
Airflow
SQL
Bash
Loops
Safe
Jan
Movement
Availability
Location
Authorized to work in
Social media
Job categories
Skills
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