AWS Architect

Quantum Technologies. LLC; Charlotte (NC); On-site; USD 89,000 - 126,000; Full time

Job summary

Quantum Technologies. LLC seeks an AWS Architect to design and deploy scalable optimization workflows on AWS. The role focuses on MILP-driven decision support, data integration, and explainability within a 12-month engagement in Charlotte, NC.

The candidate will build reproducible pipelines using Pyomo, PuLP, and AWS services, ensuring robust, auditable outputs and efficient cloud resource use.

Qualifications

• MILP modeling with Python frameworks (Pyomo, PuLP).
• Experience building scalable cloud solutions on AWS.
• Strong data integration for optimization workflows.
• Focus on reproducible results with auditable outputs.

Responsibilities

• Formulate and tune MILP models to optimize budgets, resources and portfolios.
• Package workflows as scalable cloud microservices on AWS.
• Ingest asset-health outputs to feed optimization models.
• Develop run harnesses for reproducible results and explainability.
• Leverage AI-assisted tooling to improve software delivery.

Skills

MILP modeling

Python

AWS architecture

Cloud deployment

Tools

Pyomo

PuLP

Gurobi

HiGHS

CBC

AWS Batch on Fargate

Lambda

Step Functions

S3

Terraform

Job description

US Citizen, Green Card, H-1B, OPT-EAD, CPT, Other

Job Title: AWS Architect
Location: Charlotte, NC
Duration: 12 Months + Extension
Bill Rate: $78/hour
Job Type: W-2, 1099, C2C
Client: To Be Discussed Later
Work Authorization: US-Citizen, H-1B, OPT-EAD, GC-EAD

Job Description:

• Mathematical Modeling: Formulate and tune Mixed-Integer Linear Programming (MILP) models using Python frameworks (Pyomo, PuLP) and solvers (Gurobi, HiGHS, CBC) to handle budget, resource, and portfolio constraints.
• Cloud Architecture & Deployment: Package optimization workflows as scalable cloud microservices on AWS (AWS Batch on Fargate, Lambda, Step Functions, S3) using Terraform.
• Data Integration: Ingest predictive asset-health outputs (Probability of Failure, Remaining Useful Life) to feed optimization models.
• Deterministic Execution & Explainability: Build robust run harnesses for reproducible results, diagnostic tools (constraint relaxation/infeasibility analysis), and auditable outputs (shadow prices, binding constraints).
• AI-Assisted Engineering: Utilize GitHub Copilot, Spec-Driven Development (SDD), and AI agents to enhance software delivery and build scenario-explanation tools.

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.