- Grow through mentorship and ownership
Work directly with experienced engineers who will challenge your technical depth and support increasing ownership over time.
- Build agentic features, hands-on
Partner with senior engineers to design and ship AI-powered remediation capabilities, including: - Agent workflows
- Retrieval pipelines
- Evaluation loops
- Write production code daily
Develop and maintain Java/Spring Boot microservices and AI integrations running on AWS. - Own the full lifecycle
Contribute from design doc to production release-instrumented, monitored, and continuously improved based on real usage and feedback. - Bring fresh technical perspective
Apply up-to-date knowledge of models, frameworks, and techniques to influence architecture and implementation decisions.
Technology Stack
Backend: Java, Spring Boot microservices
Cloud: AWS (Lambda, SQS, SNS, IAM, CloudWatch)
AI/Agentic Stack:
o AWS Bedrock and AgentCore
o MCP for tool integration
o RAG pipelines with vector and graph-based retrieval
o LLM-as-judge evaluation workflows
Additional languages/tools at the edges: Python, Go
Experience with multiple LLM APIs or agent frameworks
Working proficiency in Python or Go
POCs using AI for coding, automation, or data workflows
Contributions to open-source AI/ML tooling
Required Qualifications
- 4+ years of professional software engineering experience
You've shipped real production features and understand reliability, maintainability, and operational quality. - Demonstrated curiosity in AI/agentic systems
You've built with LLMs or agents (work project, side project, hackathon, or personal experiment). We care about practical exploration and what you learned. - Strong software engineering fundamentals
Solid understanding of: - Distributed systems basics
- API design and integration
- Cloud infrastructure (preferably AWS)
- Code quality and system design principles
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