Senior Software AI Engineer

Southern New Hampshire University

1d•Remote

About The Position

Southern New Hampshire University (SNHU) is seeking an experienced Senior Python Backend Engineer to join their team. This role will report directly to the Vice President of AI Engineering and will be responsible for engineering the backend systems that power their agentic AI learning platform. The engineer will build the infrastructure for the AI learning platform, ensure the Data Science team has the necessary systems for measurement and personalization, and contribute to the design and implementation of scalable APIs, data pipelines, and event streaming infrastructure. The position also involves developing LLM evaluation processes, supporting instrumentation for evaluation, participating in on-call rotations, and contributing to reliability improvements.

Requirements

5+ years production Python experience at scale
AWS expertise (Lambda, DynamoDB, S3, Glue, and Athena)
Experience with a relational database such as MySQL, PostgreSQL, or Oracle
Experience with agentic AI architectures and evaluation frameworks
Data pipeline development (streaming and batch)
Infrastructure as Code experience (CDK, Terraform)
Production SaaS experience, including participation in on-call / incident response
Experience in GitHub Actions or similar CI/CD platforms
Experience communicating updates and resolutions to customers and other partners
Active user of AI development tools (GitHub Copilot, Cursor, Claude Code, Codex) with personal projects and evolution over the past 12 months
Demonstrated examples of AI-augmented productivity gains

Nice To Haves

Enthusiasm for pushing boundaries of AI-assisted engineering

Responsibilities

Design and implement scalable APIs for agent orchestration and learner interaction
Build data pipelines that feed agent evaluation and continuous improvement cycles
Create event streaming infrastructure for real-time learner interaction analysis
Ensure data quality and accessibility for training and evaluating learning agents
Improve data infrastructure for cost and performance in close coordination with Data and ML teams
Design and implement rigorous evaluation frameworks to measure agent performance and improve cycles
Develop LLM evaluation processes and perform error analysis to identify systemic improvements for agentic learning systems
Support instrumentation that makes evaluation relevant (metrics, traces/logs, and analysis workflows)
Participate in on-call rotation and incident response for learning platform reliability
Contribute to runbooks, postmortems, and reliability improvements as we evolve our operating model