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On-Site - Los Angeles CA, United States of America
Onsite
Lendistry is an Equal Opportunity/Affirmative Action Employer. We consider applicants without regard to race, color, religion, age, national origin, ancestry, ethnicity, gender, gender identity, gender expression, sexual orientation, marital status, veteran status, disability, genetic information, or membership in any other group protected by federal, state, or local law.
If you need assistance or accommodation due to a disability, you may contact us at hr@lendistry.com
Lendistry does not accept unsolicited resumes from recruiters, employment agencies, or staffing firms. To conduct business with Lendistry, a Master Services Agreement (MSA) must be executed and confirmed prior to submitting any information relating to a potential candidate. Without a signed MSA, Lendistry shall not be responsible to any individual or entity for any payment relating to any form of fee or compensation.
And, in the event that a resume or candidate is submitted by a recruiter, an employment agency, or a staffing firm without a fully executed MSA, Lendistry has the unrestricted right to pursue and hire any of those candidate(s) without any legal or financial responsibility to the recruiter, agency, and/or firm.
A Day in the Life
The Senior AI Engineer will deliver the Lendistry AI strategy. This is a hands-on applied engineering role for an experienced LLM practitioner who can take ownership of end-to-end AI features — from design through production operation — and help set technical direction for the engineers building alongside you.
You will work directly with the VP Organizational Intelligence, the AI team lead, and the Senior Staff Engineer, AI. You will lead the day-to-day delivery of agentic workflows, document intelligence, retrieval systems, and borrower- and operator-facing AI experiences, and you will help mentor more junior AI engineers on the team. You will contribute to and shape the shared AI platform — the prompt registry, tool-calling framework, evaluation harness, and inference routing layer — that every Lendistry product team consumes.
Lendistry: Who We Are
We’re proud to be the nation’s largest minority-led, tech-savvy lender for small businesses and commercial real estate. As a certified Community Development Financial Institution (CDFI) and Community Development Entity (CDE), our mission is all about creating economic opportunities and fueling growth for small business owners and their communities. Join us as we pave the way with innovative financing and financial education!
What You’ll Be Doing
As a Senior AI Engineer on the Lendistry AI team, you will lead the delivery of:
Document intelligence pipelines that read loan applications, tax returns, bank statements, and financial statements with human-level comprehension and full audit trails.
Underwriting copilots that surface risk signals, policy checks, and recommended conditions in real time for Lendistry underwriters.
Borrower-facing conversational AI that helps small business owners navigate applications, understand decisions, and manage their loans.
Shared AI platform components — prompt registry, tool-calling framework, evaluation harness, retrieval infrastructure, and the inference routing layer that every product team consumes.
The evaluation and observability layer that turns AI reliability from a hope into a measured, managed property of the system.
Own end-to-end LLM features — from requirements through design, implementation, evaluation, deployment, and production operation — across origination, underwriting, servicing, and borrower experience.
Lead the design of new agentic workflows — LLMs that plan, call tools, evaluate results, and iterate across multi-step lending tasks with appropriate human-in-the-loop controls.
Maintain, debug, and improve existing LLM-powered features already running in production — prompt pipelines, retrieval systems, and the document intelligence stack.
Fine-tune and adapt foundation models (including LLaMA-family open-weight models and Bedrock-hosted models) to Lendistry-specific tasks using LoRA, QLoRA, instruction tuning, and prompt optimization techniques.
Design and build RAG systems end to end — chunking strategies, embedding model selection, vector retrieval, hybrid search, and re-ranking — tuned for financial documents and lending policy.
Lead the development of document processing pipelines that extract structured data from PDFs, scanned images, and other unstructured financial documents using a combination of OCR, layout understanding, and LLM-based extraction.
Design validation, confidence scoring, and fallback mechanisms that make AI outputs safe to use in regulated, high-stakes financial decisions — with clear audit trails and escalation paths.
Diagnose and resolve agentic failure modes — non-determinism, prompt sensitivity, tool misuse, looping, context-window exhaustion, and retrieval gaps — and build the patterns that prevent recurrence across the team.
Contribute to and shape the shared AI platform — the prompt registry, tool-calling framework, evaluation harness, retrieval infrastructure, and inference routing layer owned by the AI team.
Design evaluation frameworks that measure model quality, output reliability, retrieval accuracy, and regressions across iterations — golden sets, LLM-as-judge scoring, and human-review harnesses.
Instrument AI systems with observability — logging, metrics, traces, token and cost accounting, drift monitoring, and alerting on accuracy, latency, and failure modes.
Manage cost and latency at the feature level — token budgeting, response caching, model-tier routing, and batching strategies — treating cost as a first-class engineering constraint.
Partner with the AI team lead and Senior Staff Engineer, AI to translate AI strategy and architectural direction into shipped, reliable features.
Collaborate with product, credit, underwriting, and platform engineering to translate business requirements into reliable LLM system designs.
Mentor more junior AI engineers through design reviews, code reviews, and pairing — raising the bar on prompt engineering, evaluation discipline, and responsible AI development.
Lead proof-of-concept work to validate new AI use cases quickly, measure real business impact, and scale what works into production.
Lendistry AI engineers are expected to be among the most effective users of AI tools in the company. This is how we ship.
Daily use of AI coding assistants — Claude Code, GitHub Copilot, Cursor, or equivalents — as a standard part of the development loop for code generation, refactoring, testing, documentation, and review.
Human in the Loop: Follow human review process – AI engineers must maintain clear judgment and utilize established criteria for about when to trust, verify, or override AI-generated suggestions, outputs, consistent with Lendistry’s AI usage policies and applicable regulatory requirements, particularly in security-contexts involving lending decisions, borrower data, or other sensitive and business-critical contexts and financial information.
Leadership in adopting and sharing emerging agentic development tools across Lendistry engineering.
Familiarity with agentic development concepts — multi-step task automation, LLM tool use, prompt engineering for code generation, and the integration of AI agents into engineering workflows.
Your Areas of Knowledge and Expertise
Builder mentality. Bias toward shipping production systems; pragmatic about tradeoffs between model quality, latency, and cost.
Ownership. Takes features from prototype through production, operates what you build, and owns the outcome.
Rigor. Measures quality instead of eyeballing it; builds evaluation before declaring victory.
Mentorship. Elevates the engineers around you through reviews, pairing, and durable technical habits.
Communication. Explains AI behavior and limitations clearly to product, credit, and business partners.
Responsible AI judgment. Thinks seriously about safety, fairness, auditability, and the real-world consequences of lending decisions.
Comfort with ambiguity. Thrives in a fast-moving environment where the AI landscape shifts monthly and priorities evolve with it.
5+ years of software engineering experience, with 3+ years building and shipping LLM-powered applications in production.
Expert-level Python for production systems — clean architecture, type-safe data modeling (Pydantic or equivalent), clean async patterns, and testable design.
Deep hands-on production experience with at least one major LLM provider — AWS Bedrock, Anthropic Claude, OpenAI GPT, Google Gemini, or equivalent — including tool/function calling, structured output, and streaming.
Proven track record designing and operating RAG systems end to end — chunking, embeddings, vector databases (Qdrant, Pinecone, Weaviate, OpenSearch, or pgvector), retrieval, and re-ranking — including measuring and improving retrieval quality.
Demonstrated experience leading agentic workflows in production — LLM agents that call tools, reason across multiple steps, and autonomously complete multi-stage tasks with appropriate safeguards and audit trails.
Hands-on experience with fine-tuning and adaptation — LoRA, QLoRA, instruction tuning, or preference tuning — and with rigorous evaluation of model outputs rather than demo-driven validation.
Strong LLM tooling fluency — LangChain or LangGraph, LlamaIndex, DSPy, Hugging Face — with the judgment to pick the right tool and the willingness to build custom when the tool is wrong.
Production experience with unstructured data — extracting, classifying, and generating structured outputs from text-heavy inputs, including documents, forms, and scanned images.
Cloud and deployment depth — AWS preferred (including Bedrock), containerization (Docker), and hands-on experience with self-hosted LLM serving (vLLM, TGI, Ollama, or similar).
Evaluation discipline — ability to design evaluation frameworks for non-deterministic systems, build golden sets, and reason about output quality at scale.
Strong debugging instincts for LLM-specific failure modes — hallucinations, retrieval gaps, prompt drift, latency spikes, and cost regressions.
API and service design experience — exposing AI capabilities as reliable internal APIs with clear contracts, error handling, and cost controls.
Working knowledge of LLM security concerns — prompt injection, data exfiltration, output filtering, and secure inference for sensitive workloads.
Discipline around PII and sensitive financial data — PII detection and redaction, data minimization, and deployment patterns that keep sensitive data inside Lendistry's trust boundary.
Experience in fintech, lending, banking, healthcare, or another regulated or data-sensitive industry.
Experience fine-tuning LLaMA or similar open-weight models on domain-specific corpora.
Familiarity with document understanding models (LayoutLM, Donut, Nougat) and modern OCR tooling (Textract, Tesseract, or equivalents).
Background in NLP tasks such as named entity recognition, classification, or semantic similarity.
Experience building and operating shared AI platforms (prompt registry, evaluation harness, routing layer) consumed by multiple product teams.
Experience mentoring engineers and leading design reviews.
B.S. or M.S. in Computer Science, Machine Learning, or equivalent experience.
Why You'll Love Working Here:
Comprehensive Medical, Dental, and Vision Insurance
Generous Paid Time Off
Birthday Day Off
12 Paid Company Holidays
401(k) Match
FSA and HSA
Paid Life Insurance
Paid Disability Insurance
Pet Insurance
Employee Assistance Program (EAP)
Professional Development Courses
In Office Provided Snacks and Drinks
Gym Facilities (LA & Tustin/CEC Offices)
In Office Engagement Activities
Compensation Range
The US base salary range for this full-time position is $111,200 - $185,000 annually.
Our salary ranges are determined by role, level, and location.
The range displayed on each job posting reflects the minimum and maximum base salary for new hires for the position across all US locations. Within the range, individual pay is determined by multiple factors like job-related skills, experience, and state of residence. Your recruiter can share more about the specific salary range during the interview process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include any variable compensation elements.
Physical Requirements
This is a stationary position that requires frequent sitting (approximately 95%), repetitive wrist motions, grasping, speaking, listening, close vision, and the ability to adjust focus. It also may require occasional standing, lifting, carrying of 20lbs or less, walking, kneeling, bending/stooping, twisting, pulling/pushing, and reaching above the shoulder. Employees in this position must be physically able to efficiently perform the essential functions of the position.
ACKNOWLEDGEMENT
B.S.D. Capital, Inc. dba Lendistry is an equal employment opportunity employer committed to providing its employees, applicants and other covered persons with equal opportunities without regard to race, color, age (40 or older), religious creed (including religious belief, practice or dress and grooming practices), national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender (including pregnancy, childbirth or medical condition related to pregnancy or childbirth), gender expression, gender identity, sexual orientation, military or veteran status (including past, current or prospective service), or any other characteristic protected under applicable federal, state or local law.