AI Engineer

YIELD SOLUTIONS GROUP LLC•Centennial, CO

1d•$130,000 - $160,000•Onsite

About The Position

Yield Solutions Group (dba RefiJet) is a leading auto-refinance BPO that processes a high volume of applications monthly through a network of lending partners. The company operates at the intersection of financial services, technology, and customer experience, with AI being a core component of its scaling strategy. This position is within the Technology Department, which integrates Product, Development, DevOps, AI/Data, IT, and Support. The AI Engineer will report directly to the AI / Data Lead and will be involved in the full spectrum of AI development, including data pipelines, model development, LLM tooling, and production deployment. This is a production-focused role where the engineer will manage systems processing real loan volume, influence borrower outcomes, and adhere to SLA expectations from lender partners. The work is highly visible, offers a short feedback loop, and the engineer will have a significant role in shaping the AI roadmap.

Requirements

3+ years of professional experience building and deploying ML or AI systems in production environments.
Strong Python proficiency with clean, maintainable, production-grade code standards.
Hands-on experience with LLMs, including context engineering, fine-tuning, retrieval-augmented generation (RAG), agent harnesses, and LLM observability.
Proficiency with ML tooling: PyTorch/TensorFlow, or comparable libraries.
Experience with cloud platforms (AWS, GCP, or Azure) and model deployment infrastructure.
Solid data engineering fundamentals: SQL, ETL pipelines, feature engineering, and data validation.
Demonstrated ability to evaluate model performance rigorously, with working knowledge of bias/variance tradeoff, drift, and distributional shift.
Clear written and verbal communication, including the ability to explain model behavior and failure modes to non-technical audiences.

Nice To Haves

Experience with auto lending, auto refinancing, or consumer credit products.
Familiarity with loan origination systems (LOS), credit decisioning, or lending infrastructure.
Expertise with creating custom skills, plugins, slash commands, hooks, or MCP servers to encode team workflows and standards.
Technical fluency with APIs, integrations, or platform-based products.
Experience working with external partners or B2B clients in a product-led organization.

Responsibilities

Designing, building, and maintaining AI-powered systems across the borrower journey, lender pipeline, and internal operations.
Partnering with the CTO and product leadership to define the AI roadmap, prioritize use cases, and align engineering investment to business outcomes.
Translating operational problems into well-scoped ML and AI problem statements and owning the solution architecture from initial design through production deployment.
Establishing internal standards for AI development, including model evaluation frameworks, validation protocols, and responsible deployment practices.
Presenting technical findings, tradeoffs, and recommendations clearly to non-technical stakeholders, including operations leadership, lender partners, and executive teams.
Building and deploying LLM-powered tools, machine learning models, and automation pipelines to enhance application processing speed, decisioning accuracy, and borrower experience.
Designing and implementing data pipelines for model training, feature engineering, and real-time inference at production scale.
Running structured experiments, defining success metrics before testing, and documenting outcomes.
Owning model performance post-launch, including building monitoring, alerting, and retraining workflows.
Identifying underperforming AI systems and driving measurable improvements.
Evaluating new tools, frameworks, and model architectures against business criteria.
Collaborating with DevOps and Data teams to enhance infrastructure for model serving, versioning, and CI/CD integration.
Contributing to engineering culture through code reviews, internal documentation, and knowledge sharing.
Working with Compliance and Operations to ensure AI outputs meet regulatory requirements for consumer lending and adverse action standards.
Partnering with lender integration teams to understand downstream consumption of AI-driven outputs.
Supporting audit and explainability requirements for models influencing credit-adjacent workflows.
Proactively identifying and escalating model risks, such as data dependency fragility, distributional drift, and edge-case failure modes.