Aledade's AI Enablement team builds and runs the platform that the rest of Aledade's AI adoption depends on. The AI Enablement Platform Engineer is the engineer accountable for the substrate: the MCP Gateway that brokers every tool call between agents and Aledade's systems, the plugin marketplace and installer that distribute agentic capability to engineers and non-engineers alike, the model gateway configuration that determines what Aledade spends on inference and how well it performs, and the telemetry that makes all of it measurable.
This is a platform role with unusually direct business consequences. The model-routing and harness-evaluation work this engineer owns is what distinguishes a diversified, cost-optimized inference strategy from a single-vendor one. It is the right role for an engineer who wants platform depth without platform abstraction: real users, real spend, measurable adoption, and a short line between a design decision and its effect.
Primary Duties:
Minimum Qualifications:
BS/BTech (or higher) in Computer Science, Engineering or a related field.
5+ years professional software engineering experience.
Production ownership of a backend service or developer platform. API gateway, service proxy, SDK/CLI, internal developer platform, or comparable, including its auth model, release process, and operational health.
Strong production experience in at least one modern application stack (Python, TypeScript/Node, Go, or similar) and modern CI/CD.
Hands-on experience with authentication and authorization for machine-to-machine traffic (OAuth2, OIDC, M2M credentials, token scoping, secret management).
Demonstrated experience instrumenting a system you own; metrics, structured logging, tracing, or usage analytics, and using that data to drive a decision.
Direct hands-on experience with one or more agentic coding tools in a production or near-production setting (Claude Code, Cursor, Cody, Copilot agents, Aider, or equivalent).
Strong written communication: comfortable producing documentation, runbooks, and educational artifacts for engineers who weren't in the room.
Preferred KSAs:
Experience authoring or maintaining MCP (Model Context Protocol) servers, Claude Code plugins, skills, hooks, or comparable LLM-tooling integrations.
Experience running an LLM gateway or inference proxy in production (LiteLLM, Bedrock, vLLM, or similar) — routing, fallback, caching, quota, and cost attribution.
Experience building evaluation harnesses or benchmarks for LLM systems, including A/B comparison of prompts, tools, or model versions.
Experience operating a package registry, plugin ecosystem, or extension marketplace — publishing pipelines, semantic versioning, compatibility, and deprecation.
Background in healthcare technology, HIPAA-regulated environments, or PHI-handling systems; familiarity with BAA and data-residency constraints on third-party tooling.
Familiarity with Aledade's stack (Python/FastAPI, Vue/TypeScript, Postgres, AWS, Auth0, Datadog, Sumo Logic) is a plus but not required.
Comfort across the full stack (frontend/backend/infra) — platform work at this stage doesn't honor team boundaries.
Experience as an early engineer on a platform whose users are internal colleagues, where adoption has to be earned rather than mandated.