Colin Curtis

About Colin Curtis

Software engineering in the AI era is a complex and fast-moving profession where team-work and clearcommunication is as important as technical skill. Delivering great customer experience and valueis the goal. In order to consistently reach that goal, cleanly-architectured systemsand a focus on cost-effective practices are the vehicle.https://colinkcurtis.github.io/software_engineer_resume/colin_curtis_software_engineer_resume.pdf

Experience

1. Software and Machine Learning Engineer

   Fidelity Investments

   Apr 2023 — Present

   Enterprise Data and AI (EDAI), AI/ML Solutions Engineering SquadCloud & Tech Stack: AWS (Bedrock, SageMaker, EKS, S3, RDS, ECR, IAM), Azure OpenAI + OpenTofu, Python (FastAPI, Flask, Pydantic, Streamlit, pandas, SQLAlchemy), Docker, Kubernetes, Jenkins, Argo, Snowflake, Datadog, OpenTelemetry, SplunkKey contributions:Designed and implemented an end-to-end automated testing pipeline for ML model deployment on AWS SageMaker, orchestrating parallel build/deploy/test/destroy workflows across multiple model server frameworks (Triton, DJL, flask) via Jenkins. Diagnosed and resolved GPU/CUDA compatibility issues across the container and infrastructure stack.Implemented enterprise-grade JWT authentication for platform security, enabling identity management and request attribution for data science workflows with fine-grained access control and audit capabilities.Modularized MLOps model server build pipeline (Triton, DJL, custom) using Docker multi-stage builds, improving CI/CD efficiency and developer velocity.Wrote signing and composition logic for pure HTTP requests to factor out CSP SDKs (boto3, etc.) that had introduced dependency complexity across our codebases.Re-architected automation code for integration and performance tests on GenAI Gateway, upgrading test runners to scheduled and manual execution with automatic environment upgrades pre-test.Onboarded GenAI Gateway to Fidelity\'s enterprise API platform, authoring documentation for internal and customer-facing use.Led an ML training/inference cost-savings initiative resulting in five-figure monthly savings.Built an SSO-authentication Flask wrapper to secure data science web applications.Tested deployed models for LLM token-usage and response-time metrics. Provided extensive code review and deployment support across teams. Handled database migrations, troubleshooting, and JIRA backlog definition.

Education

• Appalachian State University

  B.S, Physics

  2008 — 2012

• North Carolina State University

  Master of Science - MS, Physics

  2012 — 2014

Skills

• Physics
• Spectroscopy
• Mathematical Modeling
• University Teaching
• Research
• Science
• Microsoft Office
• Raman
• Applied Mathematics
• Nanotechnology
• Microscopy
• Latex
• Statistics
• Experimentation
• Computational Physics
• Scanning Electron Microscopy
• Mathematica
• Matlab
• Laboratory
• Maple
• Analytic Problem Solving
• Python
• Absorption Spectroscopy
• Fesem
• Sem
• Fluorescence Spectroscopy
• Fluorescence Microscopy
• Originlab
• Ansys Maxwell 3d
• Labview
• Optics
• Thin Films
• Laboratory Skills
• Haskell
• Statistical Mechanics
• Functional Programming
• Statistical Modeling
• R