Note: The job is a remote job and is open to candidates in USA. Lendistry is the nation’s largest minority-led, tech-savvy lender for small businesses and commercial real estate. They are seeking a Senior AI Engineer to lead the delivery of AI strategies, focusing on document intelligence, underwriting copilots, and borrower-facing conversational AI while mentoring junior engineers.
Responsibilities
- 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
- 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
- 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
- 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
- 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
- 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
- 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
Skills
- 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
Benefits
- 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
Company Overview