About Us

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 a finance, accounting, payroll and expense management solutions solution 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 owns coding agents, review agents, skills and plugins, context and memory systems, evaluation frameworks, and the feedback loops that make them better over time.

What We're Looking For

We're looking for someone with 12+ years of software engineering experience and 6+ years leading engineering teams, including a track record of building and scaling platforms or infrastructure teams.

Leadership

- Experience leading managers or multiple teams, and building teams from the ground up: recruiting, developing, and retaining engineers with care and candor.
- Experience leading distributed teams across multiple time zones and regions.
- Strong stakeholder management and the ability to influence without authority across a complex organization.
- Excellent written and verbal communication, from engineers to executives, comfort setting direction and making decisions with incomplete information.
- Experience managing budgets, headcount planning, and vendor relationships.

Technical Skills
- Deep experience in platform, infrastructure, or developer tooling,



hands-on background building and operating production systems in the cloud including containers, Kubernetes, and infrastructure as code.
- Strong grounding in distributed systems: reliability, scalability, async workflows, and failure handling.
- Working knowledge of how LLM-based systems are built: agents and tool use, prompt and context engineering, RAG, memory, evaluation, and observability.
- Familiarity with agent protocols and ecosystems and fluency with AI coding tools.
- Experience scaling AI-assisted development tools across a large engineering organization.
- Understanding of the SDLC and developer productivity systems: CI/CD, code review, testing, and deployment.

Mindset & Collaboration
- Growth mindset: curious, always learning, and open to feedback.
- Leads by example and raises the bar for everyone around them, works well across disciplines and time zones.

Nice to Have
- Experience with developer productivity metrics and tying platform investments to business results.
- Background in security, governance, or compliance for AI systems: data handling, access control, and auditability.
- Experience with trace analysis, online and offline evaluation, model routing, and cost optimization.
- Open-source or public contributions to AI infrastructure or developer tooling.

What to Expect
- We value humility and collaboration, we're looking for people who can work as part of a team, not in silos.




- We expect leaders to take ownership beyond delivery, including team health, engineering culture, and long-term platform quality.

Key Responsibilities The Impact You'll Make

- Define the vision, strategy, and multi-quarter roadmap for our AI infrastructure and agent platform, tied to measurable outcomes: developer productivity, quality, and time to production.
- Make build-vs-buy and architecture decisions across models, orchestration, retrieval, and evaluation, define how we measure success through evaluation standards, quality signals, and developer productivity metrics.
- Recruit, hire, and retain strong engineers and engineering managers, coach and develop managers and senior ICs with transparent expectations, regular feedback, and career growth paths.
- Build a team culture that works well across time zones: clear ownership, strong written communication, and healthy handoffs.
- Own delivery of platform capabilities: agent orchestration, tool and skill registries, plugin frameworks, context and memory management, RAG, trace mining, observability, and developer tooling.
- Keep delivery predictable: spot risks and blockers early, and balance near-term wins with foundational investments, ensure reliability, security, cost control, and operational excellence for production AI systems.
- Work with engineering, product, security, data, and infrastructure leaders to find high-value workflows and integrate agents into the SDLC.
- Lead adoption: onboarding, enablement, documentation, and feedback channels so teams adopt the platform and keep using it.
- Communicate clearly with executives and stakeholders about progress, tradeoffs, risks, and results.

📌 Engineering Director (Bengaluru)
🏢 Sage
📍 Bengaluru