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About This Role
Overview:
National Debt Relief (NDR) is seeking highly skilled and technically curious Senior Applied AI Engineers to join our growing Data \& AI organization. Reporting to the Director of Applied AI, these roles will be foundational members of the Applied AI team responsible for the hands\-on implementation of enterprise\-grade Generative AI (GenAI) solutions. This is an exciting opportunity to build and deploy real\-world, production\-ready AI applications that transform client experiences, optimize internal operations, and unlock intelligent automation at scale.
As a Senior Applied AI Engineer, you will own the technical execution of GenAI initiatives — from prototyping to deployment. You’ll work closely with internal product, data, engineering, and digital teams, as well as external vendors, to deliver practical solutions powered by LLMs, RAG pipelines, and conversational AI. Your work will span chatbot development, document processing, summarization, voice AI, and AI\-driven analytics. This role is ideal for AI practitioners who have recent experience deploying production\-grade GenAI solutions and want to continue building in a fast\-moving, applied environment.
Responsibilities:
- Design, prototype, and deploy Generative AI solutions across NDR’s client\-facing and internal platforms.
- Build and optimize applications using large language models (LLMs), vector databases, prompt engineering, and Retrieval\-Augmented Generation (RAG) pipelines.
- Own hands\-on development of AI\-powered chatbots using modern conversational AI frameworks, ensuring production\-grade performance, scalability, and compliance.
- Lead development of AI agents for both digital and voice channels, supporting real\-time interactions with clients and internal users.
- Work with external vendors to evaluate GenAI platforms and APIs, integrate off\-the\-shelf solutions, and drive production rollouts.
- Build proof\-of\-concept (POC) applications to evaluate emerging use cases in document classification, summarization, AI\-powered knowledge assistants, and more.
- Optimize prompt structures, parameter tuning, and model usage to improve cost\-effectiveness and accuracy of deployed AI solutions.
- Partner with the ML/AI Ops Engineer to ensure robust data pipelines, deployments, and observability for GenAI applications.
- Work closely with the Senior Generative AI Product Manager to align technical implementation with user needs, timelines, and business priorities.
- Participate in architectural design discussions with digital, product, and engineering teams to align AI integration across the enterprise.
Qualifications:
Education/Experience:* Bachelor’s degree in data science, Computer Science, Statistics, or a related field required.
- 6\+ years of experience in machine learning, NLP, AI engineering, software engineering, and data engineering with recent focus on LLMs / Generative AI.
- Proven, hands\-on experience building and deploying production\-grade AI chatbots in the past 2 years.
Required Skills/Abilities:* Expert\-level Python programming skills, including experience with modern ML and GenAI libraries (e.g., LangChain, Transformers, OpenAI, Hugging Face).
- Proficiency in working with vector databases (e.g., FAISS, Pinecone, Chroma, Weaviate) and building RAG pipelines.
- Experience with prompt engineering, model tuning, API orchestration, and LLM evaluation strategies.
- Solid SQL skills and understanding of how to work with data stored in platforms like Snowflake.
- Comfortable working in fast\-paced, collaborative environments with product managers, engineers, and vendors.
- Strong communication skills with the ability to explain complex AI behavior and performance to non\-technical stakeholders.
- Ability to foster a culture of collaboration, continuous improvement, and innovation.
Preferred Skills/Abilities:* Experience in financial services or a related industry.
- Experience deploying voice AI or conversational AI agents in telephony environments.
- Familiarity with Snowflake, dbt, and data pipelines used in support of AI solutions.
- Experience working with or integrating Salesforce data and workflows.
- Exposure to GenAI use cases including document understanding (OCR/NLP), summarization, or personalized insights.
- Prior experience supporting regulated environments or customer service applications.
National Debt Relief Role Qualifications:* Computer competency and ability to work with a computer.
- Prioritize multiple tasks and projects simultaneously.
- Exceptional written and verbal communication skills.
- Punctuality expected, ready to report to work on a consistent basis.
- Attain and maintain high performance expectations on a monthly basis.
- Work in a fast\-paced, high\-volume setting.
- Use and navigate multiple computer systems with exceptional multi\-tasking skills.
- Remain calm and professional during difficult discussions.
- Take constructive feedback.
- Available for full\-time position.
Compensation Information: Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for each position across the US. Within the range, individual pay is determined by work location, job\-related skills, experience, and relevant education or training. This good faith pay range is provided in compliance with NYC law and the laws of other jurisdictions that may require a salary range in job postings. The salary for this position is $150,500\.00 to $173,000\.00 About National Debt Relief:
National Debt Relief was founded in 2009 with the goal of helping an expanding number of consumers deal with overwhelming debt. We are one of the most\-trusted and best\-rated consumer debt relief providers in the United States. As a leading debt settlement organization, we have helped over 450,000 people settle over $10 billion of debt, while empowering them to lead a healthier financial lifestyle and feel free to live their best life. At National Debt Relief, we treat our clients like real people. Our purpose is to elevate, empower, and transform their lives.
Rated A\+ by the Better Business Bureau, our goal is to help individuals and families get out of debt with the least possible cost through conducting financial consultations, educating the consumer and recommending the appropriate solution. We become our clients' number one advocate to help them reestablish financial stability as quickly as possible.
Want to learn more about who we are? Connect with us on social!
Benefits:
National Debt Relief is a team\-oriented environment full of rewards and growth opportunities for our employees. We are dedicated to our employee's success and growth within the company, through our employee mentorship and leadership programs.
Our extensive benefits package includes:* Generous Medical, Dental, and Vision Benefits
- 401(k) with Company Match
- Paid Holidays, Volunteer Time Off, Sick Days, and Vacation
- 12 weeks Paid Parental Leave
- Pre\-tax Transit Benefits
- No\-Cost Life Insurance Benefits
- Voluntary Benefits Options
- ASPCA Pet Health Insurance Discount
- Wellness Incentive Program
National Debt Relief is a certified Great Place to Work®! *National Debt Relief is an equal opportunity employer and makes employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or any other status protected by law.* *For information about our Employee Privacy Policy, please see* *here*
*For information about our Applicant Terms, please see* *here*
\#LI\-REMOTE
\#LI\-CB1
Salary Context
This $150K-$173K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At National Debt Relief, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($161K) sits 11% below the category median. Disclosed range: $150K to $173K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
National Debt Relief AI Hiring
National Debt Relief has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $149K - $196K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national median.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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