Senior Software Engineer II (AI Enablement)

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.

Own and extend the MCP Gateway and connector platform. Build and harden MCP server integrations (Glean, Slack, Jira, Snowflake, Salesforce, Monday, Tableau, Databricks and beyond) on the gateway; own auth, scopes, tenancy, rate limiting, and read/write policy enforcement for non-BAA tools; partner with Security and platform owners on safe-by-default access paths. Own the distribution layer, run the marketplace publishing pipeline: versioning, CI/CD, plugin PR review, release hygiene, and the developer tooling sync processes. Build and maintain shared plugins, skills, hooks, and Spec-Driven Development tooling consumed across PTA teams, plus the authorship guides that let other teams contribute without hand-holding. Own platform observability, model-gateway configuration, and cost attribution. Extend the usage-rollup and telemetry pipeline so adoption, tool-call health, and inference spend are attributable by team, workload, and model. Define model routing and build the benchmarks and harness/evaluations that let Aledade choose models on evidence. Extend the agent output and collaboration surface. Build out infrastructure to support mapping agent outputs into a browsable, searchable, commentable surface, discovery and tagging, per-viewer engagement instrumentation, comments, expiry, and share notifications. Enablement and platform stewardship. Run office hours and brownbags as a practitioner; mentor engineers on agentic-coding and plugin-authorship patterns; triage and dedupe inbound feature requests; carry on-call for marketplace-published artifacts and gateway availability.

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.

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.

Sitting for prolonged periods of time. Extensive use of computers and keyboard. Occasional walking and lifting may be required.

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