Senior CX Platform Engineer

Accountabilities

Build and operate production-grade agentic applications and multi-step AI workflows for account research, success planning, status reporting, customer onboarding, and related customer experience processes.
Develop and maintain MCP servers and secure connectors for enterprise platforms and data sources, including CRM, customer success, professional services, learning, communications, and data warehouse systems.
Use coding agents and AI-assisted development tools as part of the daily engineering workflow for planning, implementation, refactoring, testing, code review, and documentation while maintaining ownership of production quality.
Build and evolve the team's agent development harness, including shared conventions, reusable skills, commands, hooks, subagents, and continuous integration checks.
Establish evaluations, tracing, observability, and human-in-the-loop controls to make AI systems measurable, auditable, secure, and reliable when handling customer and business data.
Develop and maintain the application platform, including AWS infrastructure, gateway capabilities, Terraform configuration, deployment processes, and core applications and integrations.
Deliver production-quality Python and TypeScript applications with robust testing, CI/CD, monitoring, security controls, documentation, and operational runbooks.
Design applications and platform components for maintainability and handoff, emphasizing shared ownership and reducing reliance on individual engineers.
Partner directly with Customer Success, Digital Success, Professional Services, Education Services, and Training teams to prototype solutions rapidly and bring valuable applications into production.
Translate field and stakeholder feedback into reusable technical patterns, reference implementations, development paths, and enablement materials.
Contribute to platform and product improvements, provide feedback to Product and Engineering teams, and participate in upstream or open-source contributions where appropriate.
Mentor engineers and non-engineering stakeholders on agentic development practices while raising the team's standards for architecture, testing, security, and code review.
Requirements
Demonstrated experience building and operating production software, including ownership of substantial projects from ambiguous requirements through deployment and ongoing operation.
Hands-on professional experience using coding agents such as Claude Code, Cursor, Codex, GitLab Duo, or comparable AI development tools in production engineering workflows.
Practical experience building with large language models beyond conversational interfaces, including tool use, MCP servers, agent loops, retrieval and context design, prompt engineering, workflow orchestration, and AI evaluations.
Strong Python development skills with FastAPI, Flask, or comparable frameworks, alongside TypeScript experience with Vue, React, or similar frontend technologies.
Experience designing APIs, authentication and authorization mechanisms, and secure systems for handling customer and business data.
Hands-on experience with AWS services such as ECS, RDS, and Lambda, as well as Terraform, Docker, and applications operating behind SSO platforms such as Okta.
Experience integrating enterprise systems through REST and GraphQL APIs.
Strong understanding of Git-based development workflows, merge requests, automated testing, and CI/CD practices.
Ability to translate complex workflows from non-engineering stakeholders into clear technical plans and scalable platform solutions.
Strong asynchronous communication skills, including experience writing design documents, architecture decisions, technical documentation, and enablement materials.
Ability to balance rapid experimentation with production reliability, security, observability, maintainability, and long-term ownership.
Experience with agent harness customization, enterprise AI governance, data controls, customer success or professional services platforms, or learning technologies is an advantage.
Familiarity with platforms such as Salesforce, Gainsight, Kantata, Thought Industries, Snowflake, or comparable enterprise systems is a plus.
Technical writing, public speaking, open-source contributions, or demonstrated thought leadership in agentic AI development are valued.
Benefits
Remote work environment for eligible employees in the United States.
Base salary range of $139,200–$235,200 USD, depending on level, experience, skills, knowledge, abilities, equity considerations, and geographic location.
Equity compensation and employee stock purchase plan.
Flexible paid time off.
Parental leave and wellness-focused support.
Growth and development funding to support ongoing professional learning and career growth.
Team member resource groups and an inclusive, collaborative work environment.
Opportunities to work on production AI systems with meaningful operational impact.
Exposure to modern AI development practices, enterprise platforms, cloud infrastructure, and agentic technologies.
Opportunity to collaborate with distributed teams across Customer Success, Professional Services, Education Services, Training, Product, and Engineering.