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About This Role
AmeriSave Mortgage Corporation is a leading fintech lender transforming the way people finance their homes. With a strong foundation in digital innovation, AmeriSave is expanding beyond mortgages to offer home equity loans and personal loans—covering every type of consumer loan. Our mission is to simplify and modernize the lending experience through technology, automation, and AI\-driven solutions. We are a fast\-paced, customer\-focused organization that values transparency, agility, and excellence.
Responsibilities:* Design, develop, and maintain robust and scalable web applications using TypeScript, HTML, CSS, Java, and Node.js in a microservice environment.
- Collaborate with cross\-functional teams to define, design, and ship new features quickly.
- Write clean, maintainable, and efficient code in TypeScript and Java.
- Optimize applications for maximum speed and scalability.
- Troubleshoot and debug applications to ensure optimal performance.
- Implement data storage solutions using MS SQL and ensure data integrity.
- Participate in code reviews to maintain code quality and ensure best practices.
- Stay up\-to\-date with the latest industry trends and technologies to bring innovative solutions to the table.
- Work in the CST timezone to ensure seamless collaboration with the team.
- Design and develop user interfaces with HTML, CSS and/or generated by JSON.
- Perform DOM manipulation to create dynamic and interactive web pages.
- Ensure cross\-browser compatibility and responsiveness of web applications.
AI\-Specific Responsibilities:* Build and deploy AI\-powered applications using LLMs, GenAI agents, and retrieval\-augmented generation (RAG) pipelines.
- Integrate AI services into full\-stack solutions using frameworks like LangChain, FastAPI, or Azure OpenAI.
- Handle unstructured data (PDFs, HTML, audio, images) and multimodal models.
- Implement LLMOps practices including prompt versioning, caching, observability, and cost tracking.
- Collaborate with AI/ML engineers and data scientists to embed intelligent automation and decisioning into enterprise workflows.
- Contribute to the development of semantic search, vector databases (e.g., Pinecone, Supabase), and AI\-enhanced user experiences.
Requirements:* Experience: Minimum of 5 years of professional experience in full stack development.
- Technical Skills:
- + Proficient in HTML, CSS, and DOM manipulation for frontend development.
+ Strong experience with Node.js and Java for backend development.
+ Expertise in TypeScript for both frontend and backend development.
+ Solid understanding of MS SQL, including database design, querying, and optimization.
+ Familiarity with modern web development practices and tools.
+ Experience integrating AI APIs (e.g., OpenAI, Hugging Face, Mistral).
+ Understanding of LLMs, prompt engineering, and AI orchestration frameworks.
- Problem\-Solving: Strong analytical and problem\-solving skills.
- Collaboration: Excellent communication and teamwork skills, with the ability to work effectively in a remote environment.
- Attention to detail in evolving requirements and stability of services.
- Time zone: Availability to work in the CST time zone is mandatory.
Preferred Qualifications:* Experience with other frontend frameworks/libraries, including micro frontends.
- Familiarity with Redis, pub/sub concepts.
- Familiarity with containerization technologies such as Docker.
- Knowledge of cloud platforms, preferably Azure.
- Experience with CI/CD pipelines and DevOps practices.
- Experience with AI\-enhanced UI/UX design and intelligent user flows.
- Exposure to vector databases, semantic search, and AI observability tools.
- Experience with BytePro LOS and Asterisk telephony systems.
- Familiarity with mortgage compliance frameworks such as HMDA, TRID, RESPA, and ECOA.
High\-speed internet required for remote work, Cable or Fiber ONLY with the ability to connect via Ethernet. Minimum speeds: 70/30 Mbps (basic), 200\-300/35\-70 Mbps (shared), 500\-1,000/100\+ Mbps (heavy use).
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\*\*Please note that the compensation and benefit information that follows is a good faith estimate for this position only and is provided pursuant to applicable state and local laws on pay transparency. It is estimated based on what a successful applicant in the relevant state might be paid. \*\*
Compensation:
The annual salary for this position generally ranges between $150,000 – $200,000\.
Benefits:
- 401(k)
- Dental insurance
- Disability insurance
- Employee discounts
- Health insurance
- Life insurance
- Paid time off
- 12 paid holidays per year
- Paid training
- Referral program
- Vision insurance
Supplemental pay types:
- Referral bonuses
AmeriSave is an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
California Consumer Privacy Act Disclosure Acknowledgment*Employment Applicants, New Hires, and Employees Residing in California*
AmeriSave Mortgage Corporation’s Privacy Policy Statement (“Policy”) can be reviewed here: www.amerisave.com/privacy\-policy
AmeriSave Mortgage Corporation’s California Consumer Privacy Act (“CCPA”) Recruitment Disclosure can be reviewed here: https://www.amerisave.com/ccpa\-recruitment\-disclosure/
When AmeriSave’s Human Resources Department makes future requests for personal information, the same Policy is applicable. By applying, you understand this acknowledgment covers current and future personal information requests. You also acknowledge the business purpose of the personal information collected and that future requests may occur while applying for a position at AmeriSave and/or during employment, if applicable.
Salary Context
This $150K-$200K range is below the median for AI Software Engineer roles in our dataset (median: $189K across 518 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 26,159 AI roles we're tracking, AI Software Engineer positions make up 2% of the market. At AmeriSave Mortgage, 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 $235,100 based on 665 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($175K) sits 26% below the category median. Disclosed range: $150K to $200K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
AmeriSave Mortgage AI Hiring
AmeriSave Mortgage has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Remote, US. Compensation range: $200K - $200K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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