Sr AI/Agentic Engineer

Lendistry

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.

Who We Are

We are 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

• 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.
• Lead the development of underwriting copilots that surface risk signals, policy checks, and recommended conditions in real time for Lendistry underwriters.
• Lead the development of borrower-facing conversational AI that helps small business owners navigate applications, understand decisions, and manage their loans.
• Contribute to and shape the shared AI platform — the prompt registry, tool-calling framework, evaluation harness, retrieval infrastructure, and inference routing layer that every product team consumes.

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.

Core Experience

• 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.
• Security & Regulated-Industry Awareness: 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.

Preferred Qualifications

• 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.