Bengaluru (Hybrid – 3 days a week in office)

Sage is a global leader in accounting and business management software, trusted by millions of businesses worldwide. Together with our Bengaluru engineering team, we are building finance, accounting, payroll and expense management solutions that simplifies how businesses track and reconcile spending.

Thousands of finance teams globally use our product to streamline expense management, integrate seamlessly with leading accounting platforms, and are recognized for its clean design, usability, and speed.

We're investing heavily in AI — building the platform that lets AI agents and assistants do real engineering work, reliably and at scale. This team designs and runs the infrastructure behind our coding agents, review agents, and internal AI assistants: the orchestration, context and memory systems, evaluation frameworks, and feedback loops that make them better over time.

We're looking for someone with 3+ years of software engineering experience (mid-level) or 6+ years (senior), with hands-on experience building LLM-powered or agent-based systems in production.

Solid software engineering fundamentals: system design, API design, data modelling, and clean, maintainable code.

- Experience building and running production systems in the cloud (AWS, GCP, or Azure), including containers, Kubernetes or similar, and infrastructure as code.
- Hands-on experience with distributed systems: queues, async workflows, concurrency, reliability, and failure handling.
- Practical experience building with LLMs: tool use, agents, prompt and context engineering, RAG, and evaluation.
- MCP)



and with using AI coding tools day to day.

Transparent written and verbal communication, including explaining trade-offs to both technical and non technical audiences.

● At the senior level, we also expect you to lead projects and set technical direction, mentor other engineers, define evaluation and quality standards, and influence architecture across teams.

you help the whole organization build software better and faster.

Experience with trace analysis or log mining to find and fix agent failure modes.

- Experience building internal platforms or SDKs that other teams adopted.
- Experience with model routing, cost optimization, or prompt caching.
- Open-source or public contributions in agent tooling or LLM infrastructure.

We expect engineers to take ownership beyond just writing code — including tests, documentation, and long-term system quality.

#Develop coding agents and review agents that work well on real, large codebases, and integrate them into the existing SDLC (IDE, CI/CD, code review, issue tracking).

- Design context and memory management: retrieval (RAG), summarization and compression, session state, long-term memory, and repository-aware context.
- Build evaluation frameworks — both offline (benchmarks, regression suites) and online (production quality signals) — so we can measure whether agents are improving.
- Own observability for agent systems: tracing, cost and latency tracking, and quality monitoring.
- Work with engineers and stakeholders to identify high-value workflows, ship quickly, and iterate on real usage data.

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📌 Engineering Analysis Manager (Bengaluru)
🏢 Sage
📍 Bengaluru