Southern New Hampshire University is a team of innovators focused on transforming lives through education. They are seeking a Senior Software AI Engineer to design and implement scalable backend systems for their AI learning platform, ensuring data quality and infrastructure improvements to support the Data Science team.
Responsibilities:
- You will design and implement scalable APIs for agent orchestration and learner interaction
- You will build data pipelines that feed agent evaluation and continuous improvement cycles
- You will create event streaming infrastructure for real-time learner interaction analysis
- You will ensure data quality and accessibility for training and evaluating learning agents
- You will 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
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
- Enthusiasm for pushing boundaries of AI-assisted engineering