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About This Role
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:
- 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.
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.
Physical Requirements:
Sitting for prolonged periods of time. Extensive use of computers and keyboard. Occasional walking and lifting may be required.
We may use automated tools, including artificial intelligence (AI), to help organize and evaluate application materials. These tools support our recruiters and hiring managers by helping manage large applicant pools. Human judgment plays an essential role in our hiring process, including in the oversight and use of any automated tools. If you would like more information about our screening and hiring process, please contact us. Our Commitment to Authenticity. We use Endorsed, a fraud detection tool, to validate the authenticity of applications and protect against identity fraud. This ensures we are connecting with real people and allows us to prioritize genuine candidates. When you apply to a job on this site, the personal data contained in your application will be collected by Aledade PBC using Lever, a cloud services provider located in the United States. Lever is engaged by Aledade PBC to help manage its recruitment and hiring processes. For more information about how your personal data will be processed by Aledade PBC and any rights you may have, please review Aledade PBC’s Candidate Privacy Notice. By applying for this job, you agree to Aledade's Applicant Privacy Policy available at https://www.aledade.com/privacy-policy-applicants If you have any questions about our privacy practices, please contact [email protected].
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 9,653 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Aledade, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,448 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Aledade AI Hiring
Aledade has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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