Job Summary

We are looking for a Senior AI/ML Engineer to own agents, copilots, and platform services end to end-and to raise the engineers around you as you do it. 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 join the team in India as a fully proficient engineer who takes an ambiguous-but-scoped problem from design to shipped, instrumented software with limited guidance. Building the agent is the easy part; making it reliable enough that a finance analyst trusts it on a Monday morning is the job. That means you care about evaluation as much as features, and you understand that an agent nobody can debug is an agent nobody will keep using.

What You'll Do

- Own the design and delivery of features and small services end to end-agents, copilots, and platform components-with limited guidance.
- Build agentic systems properly: orchestration, tool use, retrieval, memory and state, and the evaluations that keep them honest as models and prompts change.
- Define the evaluations, regression tests, and observability for what you ship,



and take part in on-call and incident response for systems whose output is not deterministic.
- Contribute to the internal AI platform-agent runtimes, model access and routing, tool interfaces built on open standards such as the Model Context Protocol, and developer tooling-following the paved paths our architect sets, and improving them when they get in your way.
- Partner directly with business stakeholders to scope problems and turn them into well-built, measurable solutions. Understand the workflow before you design for it.
- Mentor early-career engineers through code review, pairing, and design feedback, and hold the quality bar on the work around you.

What Success Looks Like

- You consistently ship well-designed features and services that get adopted in production, with quality, safety, and evaluation coverage you can point to.
- You need little guidance to take a scoped but ambiguous problem all the way to instrumented, working software.
- Early-career engineers around you get better faster because of your reviews and mentorship.

What You'll Add to DigitalOcean

- Engineering Depth: Roughly 4+ years of software engineering experience, with a track record of owning and shipping features or services in production-not just contributing to them.
- Applied AI Experience: Hands-on experience building LLM-powered applications-retrieval-augmented generation,



agents and tool use, prompt design-and a working understanding of evaluation and LLMOps practice: prompt and agent versioning, regression testing, and observability for non-deterministic outputs.
- Agentic Tooling: Familiarity with orchestration frameworks (LangGraph, CrewAI, AutoGen, Semantic Kernel, or equivalents), Model Context Protocol tooling, vector stores, and runtime guardrails.
- Production Fundamentals: Proficiency in at least one production language (Python, Go, TypeScript, or Java) and comfort with up-to-date cloud-native infrastructure-containers, Kubernetes or serverless, CI/CD, observability stacks.
- Evaluation Instinct: You reach for a golden dataset before you reach for a prompt tweak, and you can tell the difference between an agent that works and an agent that demoed well.
- Clear Communication: You explain technical trade-offs to engineers and to non-engineering stakeholders, and you collaborate well across time zones.

Bonus Points

- Integrating with enterprise systems-HRIS, Financial systems, GTM Systems, or similar-including their data models and permissioning.
- Contributing to internal developer platforms or productivity tooling that engineers chose to adopt.
- Agent evaluation and observability tooling (LangFuse, Arize, Braintrust, LangSmith, OpenTelemetry-based tracing, or equivalents).

This job is located in Bengaluru, India Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Senior Software Engineer I, AI/ML (Bengaluru)
🏢 DigitalOcean
📍 Bengaluru