Requirements
- 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.
Nice to Have
- Experience in healthcare, health tech, or other regulated environments with attention to HIPAA and data protection requirements.
- Experience with event-driven architecture, message queues, or stream processing.
- Knowledge of AI observability and evaluation frameworks — prompt regression testing, safety metrics, quality scoring.
- Experience with vector databases, semantic search, or embedding pipelines.
- Prior experience in a Business Systems, IT engineering, or embedded engineering context where you built and delivered within existing technical constraints rather than starting from a clean slate.
- Experience building Claude skills, plugins, or custom prompt libraries for internal business use.
- Familiarity with enterprise AI assistant or knowledge management platforms such as Glean.
