Responsibilities :

- 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, MCP integrations to internal systems (Jira, BigQuery, observability, 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, 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.

Leadership :

- Lead across teams - cross-functional design reviews, technical direction, and mentoring senior and mid-level engineers.

- Make and defend build/buy/delegate decisions on tooling.

Requirements :

- 8+ 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 equivalent, applied to at least three of : 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.

Nice to have :

- Built custom agents, subagents, or MCP servers against internal systems.

- Prompt/context engineering at the repo scale (e.g., CLAUDE.md-style instruction files, retrieval over internal docs, codebase indexing).

- Experience with LLM evaluation, regression harnesses, or accuracy pipelines.

- Observability tooling (New Relic, Datadog, Prometheus/Grafana) and agent-assisted incident triage.

- Healthcare, FHIR/HL7, or medical coding domain exposure.

- Python for tooling and data work; PostgreSQL, Elasticsearch, or Neo4j; GCP or AWS.