Software Development Engineer 3

Arintra; Bengaluru; In loco; INR 3.000.000 - 5.500.000; Tempo pieno

Descrizione del lavoro

Arintra is seeking a Senior Backend Engineer lead in Bengaluru to design, build, and own scalable backend services using Java, Spring Boot, and microservices. You will own server-side logic, data models, APIs, and integrations end-to-end, and drive high-level designs for new services and refactors.

You will also own agentic SDLC ownership, build scaffolding for agents, define review gates, monitor flow metrics, and mentor engineers across teams.

Competenze

• 6+ years in backend engineering with at least 2 in lead or tech-lead capacity.
• Strong proficiency in Java, Spring Boot, Hibernate/JPA and microservices.
• Experience with HLD/LLD and production microservices.
• Hands-on agentic SDLC experience with AI agents in recent projects.
• Solid grounding in Git, CI/CD, and automated testing.

Mansioni

• Design, build, and own backend services in Java and microservices.
• Own server-side logic, data models, APIs, and integrations end-to-end.
• Drive architecture planning (HLD/LLD) for new services and refactors.
• Own how agentic tooling is applied across the SDLC and production monitoring.
• Lead cross-team reviews, mentor engineers, and guide tooling decisions.

Conoscenze

Java

Spring Boot

Hibernate/JPA

Microservices

HLD/LLD

Agentic SDLC

Git

CI/CD

Automated testing

Cross-team leadership

Strumenti

Git

CI/CD

Descrizione del lavoro

The candidate will have responsibilities across the following functions:

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, 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, and test coverage on agent-authored changes and iterate based on 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, cross-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 equivalent, applied to at least three of: design, implementation, code review, test generation, and production debugging/monitoring.
• Practical judgment about where agents fail at 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 these 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.
• Python for tooling and data work; PostgreSQL, Elasticsearch, or Neo4j; GCP or AWS.