Job Requirements
Key Responsibilities
What you'll do
• Build agentic features, hands-on. Work alongside senior engineers to design and ship AI-powered remediation capabilities: agent workflows, retrieval pipelines, evaluation loops.
• Write production code daily. Java/Spring Boot microservices and AI integrations on AWS.
• Learn the full lifecycle. From an idea in a design doc to something running in production, instrumented, monitored, and improved based on real usage.
• Bring fresh eyes. You're closer to the newest models, frameworks, and techniques than engineers who've been heads-down in one stack for a decade - we want that perspective in the room, not just deference to seniority.
• Grow fast. Work directly with senior engineers who'll push your technical depth, and take on more ownership as you earn it.
Our stack
• Java / Spring Boot microservices on AWS (Lambda, SQS, SNS, IAM, CloudWatch). AI stack: AWS Bedrock and AgentCore, MCP for tool integration, RAG pipelines with vector and graph-based retrieval, LLM-as-judge evaluation. Some Python and Go at the edges. You won't know all of this - we're more interested in how fast you pick things up than what's already on your resume.
What we're looking for
• 4+ years building software professionally - you've shipped real features, not just coursework or side projects, and you understand what "production" demands.
• Genuine curiosity about AI/agentic systems - you've built something with LLMs or agents, even outside of work: a side project, a hackathon entry, an experiment that didn't ship. We want to hear about it.
• Solid engineering fundamentals - distributed systems basics, APIs, cloud infrastructure (AWS). You need to know what good code and good design look like.
• You experiment and iterate ideas quickly and bring new perspectives
Nice to have
• Experience with multiple LLM APIs or agent frameworks
• Python or Go
• POCs using AI for coding, automation, or data work
• Contributions to open source AI/ML tooling.
