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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # internal AI Builder-Architect - **Company:** Touchcare LLC - **Location:** Stamford, CT, United States - **Experience:** Experienced - **Salary:** $130,000.0 - $160,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Confluence, JIRA, Audit Trail, Cloud Computing, Code Review, Databases, Continuous Integration, Python (Programming Language), PostgreSQL, Reverse Engineering, Systems Integration, TypeScript, Google Cloud, ReactJS, Delivery Pipeline, Large Language Models, Claude Code, AI Coding Agents, Agentic-AI, Gitlab, AI Platforms, Material UI, Sentry, Restful APIs, Model Context Protocol, Webhooks, Testrail, Jenkins - **Published:** October 31, 2026 - **Apply:** https://startup.jobs/applied-ai-engineer-touchcare-8343426 ## About the Role Real full-stack engineering experience is the non-negotiable foundation. AI tinkering is what sets you apart., * 4-6 years of full-stack development: front end, back end, APIs, databases and cloud deployment * Solid architecture judgment: you can design systems that are secure, maintainable and ready to scale * Comfort acting as a technical lead: making the calls, partnering with external engineers, and owning outcomes without layers of process * Hands-on AI skills: you tinker with LLMs, AI agents and coding tools, and understand where they help and where they break * Strong integration experience: APIs, webhooks and connecting third-party services, including AI services like voice and LLM providers * Experience working in and modernizing existing codebases, not only greenfield work * Comfort setting up developer infrastructure: CI/CD, environments, testing and monitoring Huge plus * You're already a vibe-coding builder: you ship real things with Claude Code, Cursor or similar, and can show us * You understand MCP (Model Context Protocol) architecture, and ideally you've built MCP servers yourself * You've set up AI coding workflows or guardrails for a team, not only for yourself Our stack today We build with AI coding tools, but you need to understand what we run today, because you'll reverse engineer it and evolve it into our AI-native way of working. * Languages and frameworks: Python, React, TypeScript, PostgreSQL * Infrastructure and CI/CD: GCP, GitLab, Jenkins * Observability and quality: Sentry, TestRail * AI and integrations: LLM orchestration frameworks, ElevenLabs voice agents, REST APIs, HIPAA-compliant infrastructure * Collaboration: Jira, Confluence Nice to have * Hands-on design-system skills: building and maintaining shared UI components * Experience in healthcare, benefits or health navigation * Familiarity with HIPAA, SOC 2 or other compliance requirements for handling sensitive data * Experience working with outsourced or distributed engineering teams * Startup experience, where you wore several hats ## Description The Applied AI Engineer, our internal AI Builder-Architect, is the technical owner of a TouchCare product pod. You make sure everything we build is technically sound: architecture, security, data, and code that holds up in production. You build hands-on with AI coding tools like Claude Code, and you'll likely write more code than anyone else in the pod. You'll start from a strong foundation: over a million members and a decade of healthcare navigation and claims data. The opportunity is to rebuild core workflows around what AI makes possible, with real users in production from day one. The work runs on two tracks. The platform track sets up our AI build infrastructure: the tools, environments, guardrails and practices that let a small team vibe code safely and fast. The product track puts that infrastructure to work, building new products and bringing our existing, more conventional products into the same AI-native way of working. You partner with a Product Lead, who champions the product: its research, strategy and design. You champion the technology. You both build, and that shared ground is what makes the pod fast. You report to the VP of Product, Innovation & AI Strategy. This isn't a traditional full-stack engineering job. We want an engineer who is also an AI builder and tinkerer, excited by how fast software can now be made. Problems you'll work on * Healthcare navigation agents that take on multi-step work like claims triage, benefits navigation and billing resolution, with people stepping in where judgment matters * A smarter workspace for our health assistants and member services team: routing, member and plan context at a glance, messaging, and a full audit trail * AI for members directly, including chat and voice experiences * An AI-native integration layer: partner integrations like ElevenLabs and many more to come, plus MCP servers for our own systems so AI agents can connect to them and to each other * The safety layer that makes all of this trustworthy in healthcare: evaluations, escalation paths and production monitoring What you'll do Platform track: build the AI build infrastructure * Set up the tools, repos, environments and deployment pipelines that let the team build with AI coding tools like Claude Code * Put guardrails in place: code review, testing, security and HIPAA-safe handling of member data, so speed never costs us trust * Build evaluation and observability for AI systems: quality evals, human-in-the-loop escalation, and monitoring of how protected health information flows * Design how our systems connect to AI: build and manage third-party integrations, and give our own systems an MCP architecture so agents and tools can interoperate across TouchCare * Define the practices, templates and shared components every pod builds on * Help our outsourced engineering team move onto the same AI-native tooling Product track: build and modernize products * Build new products with your Product Lead, from first prototype to production * Work directly with operations and member services on the workflows you're improving: claims, coverage issues, billing disputes and provider coordination * Bring existing, more conventional products into the AI-native ecosystem: assess their code, plan the path, and migrate them step by step * Own architecture, scalability, integrations and data for your pod * Turn the Product Lead's prototypes into software that holds up in production