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About This Role
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 [email protected]
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.
LLM Systems Ownership
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- 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.
Agentic Workflows \& Document Intelligence
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- 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.
Platform, Evaluation \& Reliability
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- 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.
Technical Leadership \& Collaboration
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- 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.
AI\-Assisted Development Practice
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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.
Core Experience
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- 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.
Engineering \& Platform Skills
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- 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
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- 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
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- 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.
Salary Context
This $111K-$185K range is below the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Lendistry, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($148K) sits 17% below the category median. Disclosed range: $111K to $185K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Lendistry AI Hiring
Lendistry has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Santa Clara, CA, US. Compensation range: $185K - $185K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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