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About This Role
OVERVIEWWe are seeking an experienced Senior Full Stack Engineer – Commerce AI with a strong background in full\-stack engineering and applied AI/LLM systems to support the client's Commerce AI team. This role sits at the intersection of technical innovation and guest experience, helping shape the strategic direction of AI\-powered commerce solutions.
The successful candidate will architect, build, and deploy AI\-native applications that directly impact high\-traffic booking flows, dynamic pricing models, and personalized guest experiences, blending deep traditional full\-stack engineering with hands\-on orchestration of Agentic AI systems using modern LLMs and integration frameworks.
This role is ideal for a highly autonomous, detail\-oriented senior individual contributor who is comfortable driving complex system design, working across dual\-cloud environments, and delivering AI solutions within a high\-traffic, revenue\-generating e\-commerce environment.
Location:, Seattle (hybrid preferred) or remote covering PST time zone
Contract Length: 1 year (extension and conversion option)
Rate: $75\-$90/h
RESPONSIBILITIES* Design, build, and scale secure, AI\-native applications that enhance the digital storefront, optimize revenue, and personalize the guest journey.
- Implement advanced Agentic AI workflows, integrating with leading LLMs (Gemini, OpenAI, Anthropic) to build autonomous systems for planning, dynamic merchandising, and guest support.
- Leverage the Model Context Protocol (MCP) framework to seamlessly and securely connect AI models with enterprise booking engines, CRM systems, and real\-time operational data.
- Write clean, maintainable, high\-performance code across the stack utilizing C\# (.NET) for high\-throughput backend microservices and SvelteKit for dynamic, accessible user interfaces.
- Architect and manage cloud infrastructure across Azure and GCP, ensuring optimal deployment, latency reduction, scaling, and cost efficiency of AI workloads in high\-traffic e\-commerce environments.
- Drive complex system design discussions, establish technical guardrails, and ensure strict adherence to data privacy, AI safety, and enterprise architecture standards.
QUALIFICATIONS* 8\+ years of professional software engineering experience.
- 3\+ years of dedicated experience building and scaling production\-grade AI\-native or LLM\-powered applications.
- Deep expertise in C\# and the .NET ecosystem for building enterprise\-grade APIs and highly available microservices.
- Proven track record building interactive, performance\-optimized e\-commerce or consumer\-facing web applications using SvelteKit (or advanced Svelte).
- Hands\-on experience deploying and managing production workloads in both Microsoft Azure and Google Cloud Platform (GCP).
- Hands\-on experience working with the Model Context Protocol (MCP) to build secure ecosystem integrations for LLMs.
- Demonstrated experience designing autonomous agents, multi\-agent orchestrations, and complex tool\-use (function calling) paradigms.
- Deep understanding of API integration, fine\-tuning patterns, and Retrieval\-Augmented Generation (RAG) using Gemini, OpenAI, and Anthropic models.
- Strong background in system design, specifically balancing trade\-offs between latency, cost, and accuracy when integrating AI into revenue\-generating e\-commerce flows.
- Proven success as an individual contributor in fast\-paced, agile environments.
Preferred
- Experience building complex, high\-transaction e\-commerce systems involving dynamic pricing, real\-time inventory management, or high\-scale loyalty programs (travel industry background is a strong plus).
- Experience building complex reasoning pipelines, semantic routing architectures, or custom retrieval logic to supply AI agents with enterprise business rules.
- Knowledge of LLM monitoring, evaluation, and observability tools.
Education
- Post\-secondary degree in Computer Science, Software Engineering, or related discipline.
*We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or other non\-merit factor. We are committed to creating a diverse and inclusive environment for all employees.*
Salary Context
This $156K-$187K range is below the median for AI Software Engineer roles in our dataset (median: $185K across 231 roles with salary data).
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 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Releady, 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 $218,500 based on 729 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($171K) sits 21% below the category median. Disclosed range: $156K to $187K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Releady AI Hiring
Releady has 2 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $187K - $270K.
Location Context
AI roles in Seattle pay a median of $228,700 across 516 tracked positions. That's 6% above the national median.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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