AWS Architect

Ethereum Technologies; Charlotte (NC); Hybrid; USD 89,000 - 126,000; Full time

Job summary

Ethereum Technologies seeks an AWS Architect to design and implement optimization-driven cloud architectures in Charlotte, NC. You will develop MILP models in Python (Pyomo, PuLP), deploy scalable microservices on AWS (Batch, Lambda, Step Functions, S3) and manage Terraform-based infrastructure.

The role emphasizes data integration for predictive asset-health outputs, deterministic execution, and explainability, with AI-assisted engineering to accelerate delivery.

Qualifications

• Experience formulating MILP models with Python frameworks.
• Proficiency in cloud architecture and deployment on AWS.
• Expertise in data integration for optimization workflows.

Responsibilities

• Formulate and tune MILP models using Python frameworks and solvers.
• Package optimization workflows as scalable AWS-based microservices using Terraform.
• Ingest predictive asset-health outputs to feed optimization models.
• Build deterministic run harnesses and explainable outputs for audits and shadow prices.
• Leverage AI-assisted development tools to enhance software delivery.

Skills

MILP modeling

Python (Pyomo, PuLP)

Optimization

Tools

AWS

Terraform

AWS Batch

Lambda

Step Functions

S3

Gurobi

HiGHS

CBC

Pyomo

PuLP

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.