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About This Role
Aledade's AI Enablement team enables, educates, and supports the Product, Tech, and Analytics (PTA) organization in adopting developer\-centric AI tools (Claude Code, MCP, plugins, skills, hooks). The Forward\-Deployed Engineer (FDE) embeds directly with a PTA cohort — Point of Care, Risk, Data, or another team — to unblock AI adoption on the ground: shipping plugins and skills tailored to that team's workflows, integrating their tools through the MCP Gateway, and turning hard\-won lessons into reusable patterns for the broader platform. This is the role for engineers who think like internal consultants: meet teams where they are, build what they need today, and feed durable wins back into the marketplace and installer that the rest of Aledade depends on.
### Primary Duties:
- Embed with a PTA cohort and ship targeted enablement. Pair with engineers, PMs, and analysts on the assigned team; build plugins, skills, hooks, and connectors that solve their highest\-friction workflows; measure adoption and impact.
- Productize wins into the platform. Generalize cohort\-specific work into reusable plugins, skill templates, and patterns published to the Claude Code Plugin Marketplace; document authorship guides; reduce the activation cost for the next cohort.
- Integrate new tools through the MCP Gateway \+ AWS connector layer. Add and harden MCP server integrations (Glean, Slack, Jira, Snowflake, Salesforce, Databricks, etc.) to the gateway; partner with security and platform owners on auth/scopes/observability.
- Feed back to the platform team and AI Unlock program. Contribute to roadmap and quarterly milestones; participate in office hours and brownbags as a practitioner; surface blockers (security, BAA, cost, throttling) early.
- Support adoption health. Help close the long tail of repos with no .claude/ config; mentor cohort engineers on agentic\-coding patterns; on\-call rotation for marketplace\-published artifacts.
### Minimum Qualifications:
- BS/BTech (or higher) in Computer Science, Engineering or a related field.
- 3\+ years professional software engineering experience
- Production experience with at least one modern application stack (Python, TypeScript/Node, Go, or similar) and modern CI/CD.
- Demonstrated ability to ship end\-to\-end in unfamiliar codebases — e.g., consulting, forward\-deployed, solutions, or platform\-adjacent backgrounds.
- 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 KSA’s:
- Experience authoring or maintaining MCP (Model Context Protocol) servers, Claude Code plugins, skills, hooks, or comparable LLM\-tooling integrations.
- Background in healthcare technology, HIPAA\-regulated environments, or PHI\-handling systems.
- Prior FDE / customer\-facing engineering / solutions architect / TPM\-with\-IC\-chops experience.
- Familiarity with Aledade's stack (Python/FastAPI, Vue/TypeScript, Postgres, AWS, Auth0, Datadog, Sumo Logic) is a plus but not required.
- Experience integrating tools through API gateways, OAuth/M2M flows, or service meshes.
- Comfort across the full stack (frontend/backend/infra) — FDE work doesn't honor team boundaries.
### 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.
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 3,824 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Aledade, Inc., 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 $234,620 based on 682 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Aledade, Inc. AI Hiring
Aledade, Inc. has 6 open AI roles right now. They're hiring across AI Software Engineer, AI Product Manager, AI/ML Engineer. Based in Remote, US. Compensation range: $230K - $230K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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.
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