Applied AI Engineer, Carrot Fertility

• 6–10 years of software engineering experience with strong fundamentals in data structures, system design, SQL, and application integration. Experience in a Business Systems, Enterprise engineering, or internal-facing engineering context is a strong plus
• Proven track record of shipping production software — not just prototypes. You own reliability, observability, and the operational health of what you build
• Highly proficient with Claude Code. You use AI-assisted development as a core part of how you work, not as an occasional shortcut
• Hands-on experience building and deploying agentic AI systems — including LLM orchestration, tool/function calling, RAG pipelines, and MCP server development. You understand agent reliability patterns and evaluation frameworks
• Programming fluency in Python and TypeScript/JavaScript; comfort with Ruby a plus. You write clean, well-structured code that others can maintain
• Experience with Workato or a similar iPaas platform. You’re able to leverage high- and low-code platforms depending on the situation
• Background in enterprise applications such as Salesforce or NetSuite and the integration patterns that surround them
• Strong command of SQL and data modeling. You can navigate an unfamiliar production database schema and write reliable, performant queries independently. Experience building data access patterns and integrating across heterogeneous data sources
• Fluency with REST APIs, OAuth2, webhooks, pagination, rate limiting, and error handling — the full surface area of real-world integrations
• Strong instincts for security, data privacy, and compliance — including access controls, secrets and credential management, audit logging, and data residency requirements
• Exceptional communication skills. You can move between writing a technical design document and presenting to a business leader without losing either audience
• Comfort with ambiguity and a bias toward action. You are energized by underdefined problems, not slowed down by them
• Ability to develop working familiarity with an existing application codebase and database schema — you can learn how a production system works without owning it or rebuilding it
• Experience building MCP servers or equivalent tool/API integration layers for AI systems
• Commitment to designing for maintainability and least complexity — you build things others can understand, extend, and own without needing you in the room

Desirable

• Carrot Fertility is hiring an Applied AI Engineer to join our Enterprise Technology team
• You will design, build, and ship production-grade AI and integration solutions that give internal teams reliable, structured access to Carrot's core product and operational data
• This is a hands-on engineering role — you will own delivery end-to-end: from scoping and architecture through deployment, iteration, and measurable business impact
• Your first project will be building the data access layer for Carrot's enterprise AI agent ecosystem — designing and deploying an MCP architecture that exposes structured, governed access to Carrot's core product and operational data
• As Carrot's AI capabilities grow more sophisticated, they require deterministic and programmatic access to core operational data: member eligibility, benefit balances, expense records, provider information, employer-specific rules, and more
• You will build that layer — cleanly, auditably, and in a way the broader team can maintain and extend
• This is the kind of foundational, high-leverage infrastructure work you will take on regularly
• You will be embedded with internal teams across Operations, Business Systems, and Product — translating data access needs and workflow gaps into AI-powered solutions that create lasting operational leverage
• This is not a slow-start role
• You will move with the urgency of a startup engineer and the judgment of a senior architect, while holding to a core design principle: least complexity
• Build the right thing with the right tool, and build it in a way the team can maintain and extend long after you've moved on to the next problem
• Embed directly with internal business teams to discover data access gaps and workflow pain points, prototype solutions rapidly, and own the full delivery lifecycle from scoping through production deployment
• Architect and build agentic AI systems that handle complex, multi-step business processes — producing reliable, deterministic, auditable outcomes even in high-stakes or regulated contexts
• Design systems with compliance and data governance baked in: HIPAA-compliant data handling, role-based access control, prompt hygiene, evaluation frameworks, and observability throughout
• Write high-quality, production-grade code alongside platform-based integrations. You are comfortable choosing the right tool for the job — low-code where it reduces delivery time and maintenance burden, custom code where it provides control or capabilities that low-code cannot. This is not an exclusively low-code or exclusively greenfield engineering role — it is both, applied with judgment
• Use Claude Code as your primary AI-assisted development environment, leveraging agentic coding deeply to accelerate delivery and maintain high output quality
• Translate ambiguous business requirements into clean technical designs and communicate them clearly to both technical and non-technical stakeholders
• Define and instrument success metrics for each solution: time saved, error rates, SLA improvements, cost reduction, and user satisfaction
• Feed patterns, failure modes, and reusable frameworks back into a shared internal playbook to compound team velocity over time
• Lead enablement: run knowledge-transfer sessions and create documentation so business teams can understand, trust, and extend what you build
• Build and maintain MCP (Model Context Protocol) servers that expose Carrot's core product and operational data to AI tools and agents — spanning multiple data domains
• Develop deep familiarity with Carrot's application database schema so you can independently navigate the data model, design reliable queries, and build clean access patterns without requiring constant support from Product Engineering
• Build Claude skills and plugins that business teams and AI agents can use, extend, and maintain
• Apply a design of least complexity principle to every build: prefer simple, maintainable, documented solutions over sophisticated ones, and build in a way that does not create single-person knowledge dependencies
• Application data access layer and AI agent enablement (your first project): Design and deploy a suite of MCP servers that expose structured, governed access to Carrot's core product and operational data. Data domains include expense records, uploaded files, member eligibility and benefit dates, benefit balances, merchant and currency data, journey and phase, medical and clinical questions, care recipients, employer-specific rules, and pharmacy network access. Surface this MCP layer into Carrot's AI agent infrastructure and tune the agent to meaningfully improve the quality and reliability of AI-assisted internal operations
• AI-assisted intake and routing: Build an intelligent triage system that classifies inbound requests, summarizes context, and routes or drafts responses for internal operations and customer care teams
• Revenue and finance automation: Partner with RevOps and Finance to build automations that reduce manual data work, improve forecast accuracy, and surface insights faster
• RAG-powered knowledge assistant: Deploy a retrieval-augmented system that surfaces source-of-truth documentation, SOPs, and institutional knowledge within the tools teams already use
• Automated KPI dashboards: Instrument and visualize business impact — time saved, error rates reduced, SLAs improved — across all active AI solutions