Sycamore is building the trusted agent operating system for the enterprise. Our platform helps companies build, deploy, and orchestrate agentic apps that take on real operational work, with the security and control large organizations need.

We are a small, engineering-led team working directly with Fortune 500 enterprises. You will build the shared runtime and learning systems that Sycamore's product engineers and customer applications use to create dependable agents.

The other half is the intelligence loop around that runtime: collecting useful signals from production behavior, connecting feedback to business outcomes, reproducing failures offline, evaluating changes, and turning what we learn into safer and more capable versions of agents. That can include improvements to prompts, tools, skills, context, memory, retrieval, routing, policies, and—where the evidence supports it—learning or optimization techniques.

You will build React interfaces for composing and running agents, inspecting live traces and tool calls, reviewing evaluations, resolving approvals, and understanding why a system behaved the way it did. Core AI engineers are expected to own coherent end-to-end slices across backend services, APIs, state, and user experience—not stop at a service boundary.

You will find the durable abstraction, build it without overfitting to one deployment, and operate it in production.

Infrastructure owns the execution and deployment substrate. Core AI owns the horizontal runtime, intelligence, and improvement capabilities that both depend on, along with the experiences that make those capabilities usable.

Develop the learning data plane around agents: structured trajectories, feedback and outcome signals, offline datasets, lineage, privacy controls, and reliable links between an agent version and its behavior.

Build offline analysis and improvement loops that can surface failure clusters, grow golden datasets, and propose changes to prompts, tools, skills, context, retrieval, memory, policies, and model routing.

Design experiment and versioning systems for comparing changes through replay, shadow traffic, canaries, or controlled rollouts, with clear promotion and rollback criteria.

Build detectors that identify regressions and opportunities across quality, reliability, cost, latency,



and user adoption without mistaking noisy proxy metrics for real progress.

Develop memory extraction and retrieval while enforcing tenant, user, and project visibility boundaries.

Publish reliable APIs, event-driven interfaces, reusable libraries, and pluggable improvement strategies that work across different agent categories and enterprise deployments.

Build and refine React product surfaces for agent configuration, live execution, traces, evaluations, approvals, and improvement workflows.

Own full-stack product slices across UI state, streaming, typed APIs, backend services, and persistence, with attention to accessibility, responsiveness, and failure recovery.

Debug difficult failures across models, prompts, tools, state, data, experiments, APIs, and distributed services.

Our current Core AI environment includes Python cloud services; React and TypeScript product surfaces; typed APIs and data models; relational and vector data; and cloud-native deployment.

We use coding agents, automated tests, traces, evaluations, browser automation, offline replay, cost and latency signals, and production feedback as part of everyday engineering. We are building toward a governed collect, learn, evaluate, and apply loop rather than a single monolithic training system.

We do not require previous experience with every language, framework, model provider, cloud platform, database, or infrastructure tool in our stack. Comparable experience building distributed runtimes, experimentation platforms, retrieval or recommendation systems, workflow engines, developer platforms, or production AI systems is highly relevant.

5-12 years of software engineering experience. Strong backend and distributed-systems fundamentals, including typed API design, asynchronous workflows, persistence, reliability, and production debugging.

- Practical experience shipping LLM, agent, search, retrieval, recommendation, experimentation, workflow, or developer-platform systems into production.




- An empirical approach to AI quality: you can form a hypothesis, design a useful evaluation, interpret noisy evidence, and distinguish a real improvement from movement in a proxy metric.
- AI-native. You use coding agents and modern models as a force multiplier while still owning architecture, correctness, evidence, and operational outcomes.
- You can work with Product, Infrastructure, customer engineering teams, and applied AI researchers without losing technical depth.
- A generalist mindset and comfort moving between libraries, services, data, experiments, integrations, evaluations, and product surfaces.
- Comfort building product-facing software. You can work in React and TypeScript when a capability needs a outstanding interface, and you can reason about streaming state, accessibility, and end-to-end user experience.
- Experience with reinforcement learning, preference learning, reward modeling, post-training, continual learning, ranking, causal inference, or large-scale experimentation is valuable but not required. Engineers from agent-runtime, distributed-workflow, retrieval, recommendation, experimentation-platform, integration-platform, developer-platform, or production applied-AI backgrounds often do well here. We care more about the systems you personally built, measured, and operated than a particular company, school, language, or model vendor.

It is not a prompt-engineering role detached from software and systems engineering.

It is not pure foundation-model research. It combines empirical AI work with production systems engineering.

It is not ungoverned online self-modification or a system that optimizes a metric without understanding its consequences.

It is not a narrow backend role disconnected from agent behavior, learning, and product outcomes.

Two 60-minute technical interviews, one focused on systems design and one on coding.

Build the cloud services that help enterprise agents learn from production experience.

Ship Core AI capabilities end to end, from cloud service and API design through the React experience customers use.

Join early enough to shape the Core AI architecture and the India engineering team.

Receive competitive cash compensation and meaningful equity in the company you are helping build.

📌 AI Application Developer (Bengaluru)
🏢 Sycamore
📍 Bengaluru