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About This Role
The Team:
At Kind Lending, our family of diverse and talented Kind Ambassadors is the driving force behind our new approach to the mortgage experience. Our Kind Ambassadors are the heart and soul of the organization and our “People Before Profits” mentality shines through in every department. Kind Lending is growing and is looking for top mortgage professionals who value opportunity, lasting business partnerships, and who live out our KIND mindset. Kind Lending is determined to provide the best service in the industry while also providing homebuyers with a great selection of products to enhance their buying experience. Backed by our trusted leadership team, Kind Lending’s friendly and professional model puts the “fun” in funding.
Position Summary:
We are seeking a skilled Mid\-Level AI Engineer to help design, build, and deploy intelligent automation solutions across the mortgage lifecycle. In this role, you will implement AI\-powered agents, contribute to process workflows using AI workflow tool(s), MCP, AWS Step Functions, and Lambda, and work across a multi\-cloud AI stack (OpenAI, Anthropic, GCP, Microsoft Azure) — including tools like Devin — to build scalable, cloud\-native automation systems. You'll work alongside a passionate team focused on transforming traditional processes into seamless, intelligent experiences.
Responsibilities:
AI Agent Development
- Design, deliver, and deploy semi\-autonomous agents (agentic workflows) that interact with internal systems and users across the mortgage value chain.
- Build context\-aware AI agents capable of handling document analysis, decision support, data extraction, and customer engagement.
- Reverse engineer existing systems, workflows, and business logic to identify opportunities for AI\-driven automation.
Workflow Orchestration \& Integration
- Build cloud\-based process flows using AI workflow tool(s), MCP, AWS Step Functions, and Lambda, integrating AI components, APIs, and mortgage software platforms (e.g., LOS, CRM, POS).
- Automate multistep business logic and exception handling with serverless functions, event\-driven triggers, and low\-code orchestration tools.
- Contribute to modular workflow design that supports iteration, monitoring, and scaling of pipelines.
- Leverage and integrate third\-party AI solutions where they accelerate delivery or extend capabilities.
AI Implementation
- Use OpenAI, Anthropic (Claude), GCP, and Microsoft Azure AI services to build and deploy AI capabilities.
- Use Devin and similar AI\-assisted development tools to support day\-to\-day engineering work.
- Integrate custom and pre\-trained LLMs to support automation in mortgage underwriting — document classification, condition management, intelligent routing, and dynamic content generation.
Data Analysis
- Perform data analysis on structured and unstructured mortgage data to support automation design and validate results.
Collaboration \& Iteration
- Partner with business SMEs, product managers, and data teams to map out automation targets and iterate on solutions.
- Maintain documentation, participate in code reviews, and ensure compliance with data privacy and regulatory standards.
- Apply "vibe coding" techniques — using AI coding assistants (e.g., Devin) to prototype and iterate quickly.
- Stay current on developments in AI, LLMs, and the mortgage industry, and bring relevant ideas to the team.
Qualifications:
- 3–5 years of professional experience in AI application development, automation engineering, or full\-stack cloud engineering.
Hands\-on experience with:
- Agentic frameworks for building task\-planning and decision\-making agents.
- Reverse engineering existing systems or processes to support automation design.
- Leveraging and integrating third\-party AI solutions into existing systems and workflows.
- AI CLI\-based development for interacting with LLMs and managing automation pipelines.
- AI workflow tool(s) (e.g., low\-code/no\-code orchestration platforms), MCP, and AWS Step Functions.
- OpenAI, Anthropic (Claude), GCP, and Microsoft Azure AI/ML services.
- AWS Lambda and serverless architecture.
- Devin or similar AI\-assisted coding tools.
- Relevant AI/ML libraries (LangChain, Transformers, boto3/GCP/Azure SDKs, etc.), JavaScript, and Python.
- RESTful APIs and cloud\-native integrations.
- Data analysis (SQL, pandas, or similar).
- Comfortable with rapid, AI\-assisted ("vibe coding") prototyping.
- Interest in and awareness of ongoing changes in the AI/LLM space.
- Familiarity with mortgage workflows or fintech automation is a plus.
Pay Disclosure:
The starting base pay range for this position is $120,000/annually. *The actual base is dependent on many factors such as work experience, business needs, market demands, and other compensation components.* The base pay range is subject to change and may be modified in the future.
Disclaimer:
This job description is not intended, nor should it be construed to be, an exhaustive list of all responsibilities, duties, skills, or working conditions associated with a job. It is intended to be a general description of the principal requirements common to the positions of this type. The incumbent may be required to perform additional duties and functions to meet Kind Lending’s needs.
Work Authorization:
Must be able to verify identity and employment eligibility to work in the U.S. without a visa sponsorship.
Physical Demands:
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. Must be able to lift up to ten pounds. Primary functions require sufficient physical ability and mobility to work in an office setting; to stand or sit for prolonged periods of time; to occasionally stoop, bend, kneel, crouch, reach, and twist; to lift, carry, push, and/or pull light to moderate amounts of weight; to operate office equipment requiring repetitive hand movement and fine coordination, including use of a keyboard; and to verbally and orally communicate to exchange information. VISION: See in the normal visual range with or without correction. HEARING: Hear in the normal audio range with or without correction.
Kind Lending is an Equal Opportunity Employer committed to workforce diversity. Qualified applicants will receive consideration without regard to race, religion, creed, color, orientation, gender, age, national origin, veteran status, disability status, marital status, sexual orientation, gender identity, or gender expression.
*\*\*\*For your security, Kind Lending will only contact you from email addresses ending in kindlending.com. Emails from any other domain should be treated as suspicious and may be phishing attempts.*
*\#LI\-Remote*
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Kind Lending, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Kind Lending AI Hiring
Kind Lending has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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