Senior Software Engineer, Internally Deployed Products

What You'll Own

MCP Server Platform

• Design, build, and operate Model Context Protocol servers that expose CRM, ticketing, analytics, and communication data to AI agents across the GTM stack.
• Implement Okta PKCE authentication flows and RBAC policy enforcement so agents access only the data they're authorized to touch.
• Maintain deployment infrastructure on AWS (Bedrock, Lambda, ECS, API Gateway) and contribute to GCP workloads where applicable.
• Own observability: structured logging, distributed tracing, latency SLOs, and on-call runbooks for every production server.

Agent Orchestration & AI-Native Products

• Build and maintain multi-step autonomous agents that execute end-to-end GTM workflows — lead qualification, deal room assembly, onboarding automation, support triage, and more.
• Architect prompt engineering frameworks, tool-call schemas, and agent evaluation harnesses that make AI behavior predictable and auditable.
• Integrate with LLM providers (Anthropic, OpenAI, AWS Bedrock AgentCore) and maintain version-pinned, cost-tracked model configurations.
• Deliver AI-powered internal applications (web apps, CLI tools, Slack integrations) that non-technical GTM stakeholders use without friction.

GTM Platform Engineering

• Own full-stack feature delivery across TypeScript/Node.js backends and React/TypeScript frontends for internal tooling.
• Write Python automation scripts, ETL pipelines, and data transformation layers that feed GTM analytics and AI context.
• Collaborate with Systems Engineering and GTM Engineering teams on cross-cutting API standards, data contracts, and integration patterns.
• Conduct code reviews, establish engineering standards, and actively mentor junior engineers toward higher leverage.

AI-Native Development Practice

• Use AI coding assistants (Claude, Cursor, GitHub Copilot) as primary engineering accelerators — not supplements — to ship at a pace that punches above a single engineer's weight.
• Document AI usage patterns, prompt templates, and agentic workflows so the team's collective throughput compounds.
• Stay current on MCP protocol evolution, agent frameworks (LangGraph, CrewAI, custom), and emerging LLM capabilities; bring back what matters.

Required Qualifications

• 5+ years of professional software engineering experience with production systems.
• Expert-level TypeScript and Node.js — idiomatic, typed, testable server-side code.
• Strong Python — automation scripts, data pipelines, and scripting for AI/ML tooling.
• Meaningful AWS deployment experience: Lambda, Bedrock, ECS/Fargate, API Gateway, IAM, Secrets Manager, CloudWatch.
• Demonstrated experience integrating with LLM APIs (OpenAI, Anthropic, AWS Bedrock, or equivalent) and shipping AI-powered features to real users.
• Solid foundation in REST API design, OAuth 2.0 / OIDC authentication, and secure credential management.
• Experience with CI/CD pipelines, infrastructure-as-code (Terraform, CDK, or SAM), and cloud cost awareness.
• Clear written communication: design docs, ADRs, and runbooks that others actually read.
• Track record of using AI tools (LLM assistants, copilots, agentic workflows) as a genuine productivity multiplier — not a gimmick.

Strongly Preferred

• Hands-on experience with MCP (Model Context Protocol) — building servers, defining tool schemas, or operating multi-server agent environments.
• GCP experience (Cloud Run, BigQuery, Cloud Functions) to complement AWS work.
• Salesforce, HubSpot, or other CRM API integration work — understanding GTM data models is a significant advantage.
• React or Next.js front-end capability to ship internal dashboards and tooling without hand-offs.
• Familiarity with agent orchestration frameworks: LangGraph, AutoGen, CrewAI, or custom orchestration patterns.
• Experience in a GTM Systems, RevOps, or Sales Engineering context.
• Okta / identity provider integration work (PKCE flows, SCIM, token management).