Principal AI & ML Architect

National Debt Relief, LLC.•,

1d•$180,000 - $207,000•Remote

About The Position

National Debt Relief is seeking a Principal AI & ML Architect to design and build AI and machine learning systems supporting its Data Science and Applied AI teams. Reporting directly to the Vice President of Data and AI, this senior individual-contributor role serves both teams, shaping shared architecture standards while remaining hands-on in code. A central focus is designing the company’s agentic architecture: frameworks, patterns, and platforms that enable AI agents to reason, use tools, and act reliably across predictive ML and Generative AI use cases. The architect will partner with the Directors of Data Science and Applied AI, Data Engineering, and Analytics Platforms to take solutions from prototype to secure, scalable production. The role combines technical architecture and production delivery in a highly collaborative environment, requiring strong teamwork and interpersonal skills. National Debt Relief is a consumer debt relief provider founded in 2009. The company says it has helped more than 450,000 people settle over $10 billion of debt and aims to help individuals and families regain financial stability. The position offers an annual salary of $180,000 to $207,000, with individual pay determined by location, job-related skills, experience, and education or training. The posting identifies the role as available full-time and includes the tags #LI-REMOTE and #LI-CB1.

Requirements

Bachelor’s degree in Computer Science, Data Science, Statistics, or a related quantitative field; a Master’s or PhD is strongly preferred.
At least 8 years of experience in ML engineering, data science, AI engineering, or software architecture roles.
At least 3 years in a senior, staff, principal, or architect-level role setting technical direction across multiple teams.
Demonstrated success designing and deploying production-grade ML models and Generative AI applications at scale.
Hands-on experience designing and shipping agentic AI systems, including LLM agents, tool use, and multi-step workflows, to production.
Expert Python skills and a track record of writing production-quality, well-tested code.
Strong SQL proficiency for data preparation and model enablement workflows; Snowflake is preferred.
Deep understanding of LLMs, RAG architectures, vector databases, prompt engineering, and agent frameworks.
Strong grounding in classical ML, including supervised and unsupervised learning, time series, anomaly detection, and NLP.
Experience with MLOps and LLMOps tooling, including model registries, experiment tracking, evaluation frameworks, and monitoring.
Experience deploying models and services via APIs using tools such as FastAPI and Docker, with AWS, Azure, or GCP cloud platforms.
Experience with version control such as Git and CI/CD practices.
Ability to influence without authority and build consensus across teams with different priorities.
Exceptional communication skills, including the ability to explain complex architecture to executive and non-technical stakeholders.
Experience applying an AI-first development approach, using AI coding assistants and agentic tools to accelerate delivery.
Computer competency and ability to work with a computer; ability to navigate multiple computer systems and multitask.
Ability to prioritize multiple tasks and projects simultaneously and work in a fast-paced, high-volume setting.
Exceptional written and verbal communication skills; ability to remain calm and professional during difficult discussions and take constructive feedback.
Punctuality and consistent attendance, and ability to meet high monthly performance expectations.
Available for a full-time position.

Nice To Haves

Master’s or PhD in a relevant field.
Experience in financial services or a related regulated industry.
Familiarity with Salesforce integrations and data workflows.
Experience with Amazon SageMaker, Azure Machine Learning, Google Vertex AI, or similar enterprise AI/ML platforms.
Experience with voice AI or conversational AI for telephony platforms.
Knowledge of AI governance, model risk management, and responsible AI practices.
Exposure to collaborative tools such as JIRA and Miro.
Snowflake proficiency.

Responsibilities

Partner with Data Science and Applied AI team members to design NDR’s agentic architecture, including agent orchestration patterns, tool and API integration standards, memory and retrieval layers, guardrails, evaluation, observability, and cost controls.
Collaborate with both teams on reference architectures and reusable components so they can build AI and ML solutions on a common foundation.
Prototype, write production-quality code, and work with engineers and data scientists to deliver high-priority solutions.
Contribute to the design of end-to-end ML systems, from feature pipelines and model training through deployment, monitoring, and retraining.
Help determine how agents and LLMs should call predictive ML models, and vice versa, to combine them into cohesive solutions.
Work with Data Engineering and Analytics Platforms to ensure data, infrastructure, and platform choices support AI and ML workloads at scale.
Contribute to build-versus-buy evaluations for AI platforms, frameworks, and vendors, prioritizing scalable, secure, and cost-effective options.
Help mature MLOps and LLMOps practices, including CI/CD for models and agents, versioning, testing, and production monitoring.
Champion governance, security, privacy, and compliance for AI systems in a regulated financial services environment.
Participate in architecture and code reviews, and document shared standards and best practices.
Share expertise with peers across the Data and AI organization, contributing to continuous learning and innovation.
Communicate architecture decisions, trade-offs, and roadmaps to technical teams and executive stakeholders.
Stay current with AI and ML tools, models, and agent frameworks, and recommend when to adopt them.