About the role
The core responsibilities for the job include the following:
Backend engineering:
- Design, build, and own backend services in Java, Spring Boot, and a microservices architecture with real accountability for performance, scalability, and robustness.
- Own server-side logic, data models, APIs, and integrations end to end.
- Drive HLD and LLD for new services and for material refactors of existing ones.
Agentic SDLC ownership:
- Own how agentic tooling is applied across our SDLC spec/design, implementation, review, testing, and production monitoring, not just at the coding step.
- Build and maintain the scaffolding that makes agents effective on a large codebase: repo-level context and instruction files, custom agents/subagents, slash commands and reusable workflows, and MCP integrations to internal systems (Jira, BigQuery, observability, and docs).
- Define the review bar for agent-generated code: what gets human-reviewed, what gets gated by tests, what never gets delegated.
- Instrument and evaluate the workflow cycle time, review turnaround, escape defect rate, and test coverage on agent-authored changes, and iterate on the basis of that data, not vibes.
- Raise the team's ceiling: onboard engineers onto these workflows, run internal enablement, and set guardrails for security, licensing, and data handling when agents touch source code and production data.
- The leadership lead across teams conducts cross-functional design reviews, provides technical direction, and mentors senior and mid-level engineers.
- Make and defend build/buy/delegate decisions on tooling.
Requirements:
- 6+ years in backend engineering, with at least 2 in a lead or tech-lead capacity.
- Strong proficiency in Java, Spring Boot, Hibernate/JPA, and microservices.
- Demonstrated experience in HLD and LLD and in designing, building, and deploying microservices-based systems in production.
- Hands-on agentic SDLC experience within the last 12 months: You have shipped production software where AI agents were a primary part of the workflow.
- Concretely, experience with tools such as Claude Code, Cursor, Codex, Devin, Copilot Workspace/agent mode, Aider, or equivalents applied to at least three of the following: design, implementation, code review, test generation, and production debugging/monitoring.
- Practical judgment about where agents fail context management on large codebases, hallucinated APIs, silently wrong tests, review fatigue, and concrete mitigations you've put in place.
- Solid grounding in Git, CI/CD, and automated testing, including how these change when a large share of diffs are agent-authored.
- Strong SDLC fundamentals and a track record of working with multiple teams.