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. The simplest way to describe this role: you are the engineer in the customer room and the product engineer who ships what the room needs.

That can include the user experience, APIs and data model, agent instructions and tools, enterprise integrations, permissions, approval points, durable workflows, and the operational controls needed to run safely.

You own the full loop: discovery, implementation, realistic testing, preview and deployment, production debugging, adoption, and iteration. When the same runtime, connector, memory, approval, or deployment need appears repeatedly, you work with Core AI or Infrastructure to turn it into a reusable capability instead of maintaining parallel custom implementations.

Sit with a customer's operators and engineering team to map a manual process, its domain entities, edge cases, permissions, approval points, and success criteria.

Build a production application across the user interface, backend APIs, data model, agent behavior, tools, integrations, and human-review surfaces.

Exercise the real workflow in a browser, run backend and end-to-end tests, inspect traces and structured events, and fix failures before users encounter them.

Deploy a preview or development version, work through customer security and networking constraints, and stay close to usage after launch.





Separate the customer's specific business behavior from a reusable platform pattern, then contribute the shared primitive with the relevant Core AI or Infrastructure engineer.

Turn ambiguous enterprise problems into transparent technical designs, working software, and measurable outcomes.

Design APIs and relational data models that accurately represent the customer's domain and remain understandable as the product evolves.

Navigate enterprise requirements around authentication, authorization, tenant isolation, data handling, deployment, observability, and change management.

Test realistic user journeys rather than stopping at isolated units or happy-path demos.

Travel within India when working alongside users will accelerate discovery, delivery, or adoption.

Our current product environment includes TypeScript, React, TanStack, Tailwind, Python, FastAPI, typed data models, PostgreSQL, durable workflows, MCP-based tools and connectors, browser automation, and cloud-native deployment on Kubernetes.

We use automated tests, coding agents, traces, evaluations, and production feedback as part of everyday engineering.

We do not require previous experience with every language, framework, or vendor in our stack. Strong engineers who understand systems, learn quickly,



and have shipped production software can become effective here without matching our tools one for one.

5-12 years of software engineering experience. Strong software fundamentals across product design, APIs, data modeling, distributed systems, testing, and production debugging.

- Evidence that you personally shipped and operated meaningful software, rather than stopping at prototypes or delegating the production work.
- Comfort moving across user interfaces, backend services, data, agent behavior, integrations, and deployment when the outcome requires it.
- A security-minded approach to enterprise data, permissions, credentials, approvals, and auditability.
- AI-native. You use coding agents and modern models as a force multiplier while still owning the architecture, understanding the code, and explaining important decisions.
- Comfort with startup ambiguity, fast feedback loops, broad ownership, and regular customer travel.

Former founders, early product engineers, and forward-deployed engineers often do well in this role when they have retained real code and production ownership. It is not a solutions role that hands requirements to another engineering team.

It is not an ML research role focused on training foundation models.

It is not a narrow role defined by one programming language or layer of the stack.

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

Work on AI systems that move from enterprise problems to production outcomes quickly.

Shape both individual deployments and the product used across customers.

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

📌 Product & Engineering (Bengaluru)
🏢 Sycamore
📍 Bengaluru