We are looking for a lead-capable AI/ML Engineer to take complex AI systems from design to production, and to anchor the technical quality of our engineering team in India.

DigitalOcean is building the substrate for an AI-native company, and the AI Engineering team is doing that work. It spans three connected pillars: internal AI copilots and agents for teams across Finance, People, Sales, Marketing, Support, and IT; the internal AI platform that powers them—model access and routing, agent runtimes, evaluation harnesses, and the developer tooling around them; and re-architecting business processes across our enterprise systems footprint to be AI-native. Our engineers in the US and India work together across time zones, each owning delivery end to end for their business domains while contributing to the shared platform.

You will be the senior-most engineer on the team in India—owning critical systems, leading multi-person workstreams, and working with our architect to set the technical bar. This is a leadership role without direct reports: your influence comes from the quality of your designs, the decisions you make when the answer is genuinely unclear, and the engineers who get better because of you.

It is also the role closest to the business. You will sit with the people whose work an agent is about to change, understand what they actually do, and design something that earns their trust. Agents that touch systems of record are unforgiving of vague requirements, and translating an ambiguous business problem into a well-bounded system is the hardest part of this job.

What You'll Do

• Own complex systems and lead multi-person workstreams end to end, making the key design decisions for the critical agents, copilots, and platform services in your domains.
• Design agentic systems with production rigor: orchestration, tool use, capability boundaries, memory and state, evaluation, observability, guardrails, and cost per completed task.
• Drive the quality, evaluation, and observability bar across the team, and lead incident response and the operational discipline for systems whose output is not deterministic.
• Contribute high-leverage code to the internal AI platform, and help evolve the paved paths in partnership with our architect—so the next engineer solves the problem once rather than again.
• Act as the primary technical partner to business owners, turning ambiguous problems into scoped, shipped systems with outcomes you can measure.
• Mentor engineers at every level, lead design reviews, and grow the technical depth and autonomy of the team around you.

What Success Looks Like

• Critical systems in your domains ship reliably and hold up in production: strong evaluation scores, low regression and incident rates, and unit economics that make sense.
• The workstreams you lead land on time, with design decisions that are clear enough that other people can build on them.
• The team operates with more autonomy and a higher engineering standard than it did before you joined.
• Business owners come to you early, because working with you has been worth it before.

What You'll Add to DigitalOcean

• Engineering Depth: Roughly 6+ years of software engineering experience, including a track record of leading the design and delivery of complex systems in production.
• Deep AI Expertise: Hands-on depth in modern AI systems—LLMs, retrieval-augmented generation, agents and tool use, evaluation—and the operational discipline of LLMOps: prompt and agent versioning, regression testing, cost attribution, and observability for non-deterministic outputs.
• Agentic Systems in Production: Real experience building or operating agentic systems—orchestration frameworks (LangGraph, CrewAI, AutoGen, Semantic Kernel, or equivalents), Model Context Protocol tooling, vector stores, and runtime guardrails. You know the failure modes because you have debugged them.
• Production Fundamentals: Strong fundamentals in at least one production language (Python, Go, TypeScript, or Java) and modern cloud-native infrastructure—Kubernetes, serverless, APIs, event-driven patterns, observability stacks.
• Governance Judgment: A point of view on making autonomous systems safe: capability boundaries, least-privilege tool access, human approval for consequential actions, audit trails, and autonomy that is earned on evidence rather than assumed.
• Business Translation: The ability to sit with a finance analyst or a recruiter, understand what they do all day, and turn an ambiguous business problem into a well-bounded system. Excellent written and verbal communication.
• Leadership Without Authority: A demonstrated ability to mentor engineers, raise a team's technical bar, and get alignment on a design without a title doing the work for you.
• Distributed Collaboration: Effective working across time zones with teammates in the US.

Bonus Points

• Re-engineering business processes in enterprise systems—HRIS, Financial systems, GTM Systems, or similar—including audited or otherwise regulated workflows.
• Deploying AI tooling at scale to internal users (Cursor, Claude Code, GitHub Copilot, or equivalent rollouts).
• Agent evaluation and observability tooling (LangFuse, Arize, Braintrust, LangSmith, OpenTelemetry-based tracing, or equivalents).
• Identity and access design for autonomous systems: short-lived machine credentials, scoped tool access, delegation with preserved provenance.

*This job is located in Bengaluru, India

JR: 2026-7956

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