Applied AI / AWS Engineer II

EchoStar•Centennial, CO

About The Position

Software quality assurance automation requires scalable, intelligent tooling to keep pace with modern IT delivery demands. The Applied AI / AWS Engineer II resolves bottlenecks in testing workflows by engineering automated utilities and integrating Generative and Agentic AI into software delivery practices. Operating within the Technology Assurance and Performance team, this role modernizes existing automation capabilities and bridges cloud infrastructure with core QA operations across globally distributed environments.

Requirements

Software engineering capabilities using Python, Java, or C# to build robust API integrations and interface with SQL and NoSQL databases
Knowledge of cloud architecture and serverless implementation on AWS, specifically utilizing AWS Lambda, API Gateways, and S3
AI Application and Literacy skills, including practical experience implementing Generative and Agentic AI frameworks to automate technical tasks
Comprehensive working knowledge of modern software delivery infrastructure, including version control (GIT) and CI/CD pipelines (Jenkins, Harness)
Strong analytical problem-solving skills and the ability to manage complex tasks independently under tight deadlines
Proven cross-functional collaboration skills with clear written and verbal technical communication capabilities across all organizational levels
Bachelor’s degree in Computer Science, Engineering, or a related field
3+ years of experience in software engineering
At least 3 years of experience with: Programming languages such as Python, Java, or C#
At least 3 years of experience with: AWS cloud services including Lambda, API Gateways, and S3
At least 3 years of experience with: API development, SQL/NoSQL databases, version control (GIT), and CI/CD tools (Jenkins or Harness)

Nice To Haves

Master’s degree in Computer Science, Engineering, or a related field

Responsibilities

Build scalable AWS cloud utilities using Lambda, API Gateways, and S3 to automate software delivery and performance testing across application environments
Design and deploy Generative and Agentic AI tooling within Quality Assurance frameworks to accelerate test coverage and improve fault detection efficiency
Construct functional prototypes to validate modern technical concepts and iterate based on cross-functional stakeholder feedback
Extend and optimize existing internal software applications to maintain high reliability and support key departmental delivery goals
Partner with globally distributed engineering teams to integrate automated testing workflows and meet key organizational targets