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About This Role
OVERVIEW: The Company U.S. Financial Technology (U.S. FinTech) is seeking an experienced Lead AI and Data Solution Engineer to drive the development, integration, and deployment of advanced AI, data engineering, and cloud-based solutions. This is a full-time remote opportunity.
U.S. FinTech built and operates the largest and most advanced mortgage securitization platform in the world, supporting the Uniform Mortgage-Backed Security (UMBS) of Fannie Mae and Freddie Mac.
Supporting 70% of the mortgage-backed securities in the market, U.S. FinTech provides best-in-class single-family issuance, bond administration, disclosure, and tax services. We support a broad portfolio of products for our clients with full lifecycle management.
Our market-leading, cloud-based, end-to-end platform executes transactions on an extraordinary scale which has bolstered liquidity in the secondary mortgage market, one of the largest and most important financial markets in the world. Our unique approach to securitization combines the best minds in financial services with the know-how, flexibility, and innovation of leading technologists. RESPONSIBILITIES : Job Information
The ideal candidate will have deep expertise in data engineering, agentic AI systems, large language models (LLMs), and Model Context Protocol (MCP). The preferred candidate will bring deep experience with AWS and Snowflake services, including a strong understanding of security best practices for cloud-based AI and data solutions. This role requires a hands-on leader who can design and code scalable, secure, and innovative data pipelines and AI solutions that deliver business value. Key Job Functions
- Solution Engineering: Lead the design, development, and deployment of enterprise-scale data and AI solutions, ensuring alignment with business objectives and technical best practices.
- LLM and Agentic AI: Architect, implement, and optimize large language models and agentic AI workflows for business automation and decision support.
- Framework Expertise: Design and deploy AI solutions using leading frameworks such as LangChain, LangGraph, and n8n for scalable agent orchestration, workflow automation, and integration with business systems
- Model Context Protocol (MCP): Develop, integrate, and manage MCP-based solutions to enhance model interpretability, context management, and deployment at scale.
- Cloud and Data Engineering: Leverage AWS and Snowflake to build scalable, secure, and efficient data pipelines for structured and unstructured data.
- Collaboration: Partner with cross-functional teams, including other technology, business, risk, legal, and compliance stakeholders, to deliver integrated solutions.
- Innovation: Stay current with emerging technologies and industry trends in AI, data engineering, and cloud computing, driving continuous improvement and innovation.
- Governance and Compliance: Ensure all solutions meet regulatory, security, and compliance requirements relevant to the financial services industry.
- Mentorship: Provide technical leadership and mentorship to junior team members.
QUALIFICATIONS: Education
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
Minimum Experience
- 8+ years of experience in data engineering, AI solution development, or related roles.
- Proven expertise in large language models (LLMs), agentic AI systems, and Model Context Protocol (MCP).
- Good working experience developing and integrating AI solutions using AWS Bedrock, including prompt engineering, RAG, and enterprise application integration.
- Hands-on experience building AI applications using Python frameworks such as LangChain, LlamaIndex, FastAPI, and AWS/OpenAI SDKs.
- Strong experience with Snowflake and AWS services (Glue, S3, Lambda, SageMaker, etc.).
- Experience in the financial services or mortgage industry is preferred.
- Applicants must be authorized to work in the US without requiring employer sponsorship currently or in the future. U.S. FinTech does not offer sponsorship for this position.
Specialized Knowledge & Skills
- Deep understanding of AI/ML frameworks, data pipelines, and cloud-native architectures.
- Hands-on experience with LLM deployment, fine-tuning, and integration.
- Advanced proficiency in Python programming with the ability to design, develop, test, and troubleshoot production-grade AI/ML applications and reusable frameworks.
- Proficiency in agentic AI design patterns and implementation.
- Expertise in Model Context Protocol (MCP) for context-aware model deployment and management.
- Strong knowledge of Snowflake, AWS, and advanced data modeling.
- Experience with data governance, security, and compliance best practices.
- Excellent communication, collaboration, and presentation skills.
- Ability to translate complex technical concepts for non-technical stakeholders.
Pay Range $200,000 to $215,000 U.S. FinTech's pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) a candidate’s qualifications, skills, competencies, and experience, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law. U.S. FinTech offers a competitive total compensation package, which includes a performance bonus, 401k match, healthcare coverage, PTO, and a broad range of other benefits. Employment As a condition of employment with U.S. Financial Technology, any successful job applicant will be required to successfully complete a background investigation, which may also include a credit check for positions in some areas of our business.
U.S. Financial Technology is an Equal Opportunity Employer. ##LI-Remote
Salary Context
This $200K-$215K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At U.S. Financial Technology, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. Disclosed range: $200K to $215K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
U.S. Financial Technology AI Hiring
U.S. Financial Technology has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $215K - $215K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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