Applied AI Engineer

Member of Technical Staff – Applied AI Engineering

EXPERIENCE: 2–4 years
TYPE: Full-time
FOCUS: Applied AI Customer-facing
WORK HOURS: US Timings
LOCATION: Bengaluru

ABOUT THE COMPANY

At Composio, we are building infrastructure that allows AI agents to communicate with the tools people use for work, including GitHub, Gmail, Notion, Salesforce, and more. We are a small team of engineers working across context, auth, execution, and reliability to build the action layer for AI agents.

We raised a $25M Series A from Lightspeed, with angel investors including Guillermo Rauch (CEO of Vercel), Dharmesh Shah (CTO of HubSpot), and Gokul Rajaram. Our customers range from YC startups to companies like Brex, Glean, Zoom, and more.

ABOUT THE TEAM

This is an applied AI engineering role, not a traditional support role. You will work directly with customers, learn the repeated work behind their problems, and build agentic systems that take that work over. When a customer needs direct engineering, you will debug and ship. When a pattern repeats, you will turn it into a workflow, eval, tool, or product capability that compounds.

• The frontier: support is one of the richest applied AI environments — an agent has to gather context, reason across systems, use tools, take bounded action, verify the outcome, and communicate clearly. You design that system.

• The trust bar: AI cannot be trusted by default with customer support. You define state, permissions, guardrails, evals, and escalation paths so the automation earns trust.

• The feedback loop: it's immediate. Deploy into real support work, watch where the system fails, and improve it until people can hand over more of the job.

ROLES AND RESPONSIBILITIES

What you'll build

• Agentic workflows that take over support functions: intake, classification, context gathering, reproduction, diagnosis, response drafting, remediation, verification, and follow-up.

• Tool-using agents that safely inspect runs, logs, auth state, configuration, and provider behavior, then propose or execute bounded recovery actions.

• Eval suites for diagnostic correctness, resolution quality, safe escalation, customer communication, and end-to-end task completion.

• Human-in-the-loop systems that make ownership, uncertainty, approvals, handoffs, and failure states explicit.