Software Development Engineer 3

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.
Leadership:
Lead cross-team functional design reviews, technical direction, and mentoring 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.
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.

Experience
6-10 yrs