Saaf Finance

Forward-Deployed AI Engineer

Remote, United States remote Entry Salary not listed
remote Technology & IT Curated
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About the role

AHL + Saaf AI — Mortgage Lending, Reimagined
Saaf AI is building the future of mortgage lending by combining cutting-edge AI with proven lending operations. Saaf AI is part of American Heritage Lending, a top-10 private lender processing billions in loan volume, with 15+ years of growth. We are backed by some of the largest asset managers and funds and are growing fast.
We're not experimenting with AI. We're deploying it. From underwriting to document processing to borrower experience, all are shaped by AI. If you want to work somewhere that uses AI as a core building material — you're in the right place.
The Role
We're hiring a Forward-Deployed AI Engineer to design and ship AI-powered products and agents from end to end. This isn't an ML research role or a prompt-engineering sandbox. It's a hands-on builder role — you own the full arc from "here's a business problem" to "here's a working AI system in production."
Mortgage lending is one of the last industries to be meaningfully transformed by AI. The problems are hard, the data is rich, and the stakes are real. You'll work directly on the things that matter most: automating underwriting decisions, building intelligent borrower-facing experiences, creating agents that navigate complex loan workflows, and using AI to compress tasks that currently take days into minutes.
This role is right for you if:
You've shipped AI-powered products or agents to production — not just prototypes

You think in systems: LLM calls, tool use, retrieval, evaluation, and feedback loops are design primitives to you

You move fast and take ownership — you'd rather build the thing than debate the spec

You want to be in a market where AI genuinely changes lives, not just optimizes click-through rates

What You'll Build
AI Agents & Autonomous Workflows
Mortgage lending is a process-heavy industry — condition management, document review, borrower communication, underwriting analysis. You'll design and ship agents that automate or accelerate these workflows end to end:
Build multi-step LLM agents that can navigate loan files, evaluate conditions, flag exceptions, and surface recommendations to underwriters

Design agentic workflows with tool use, structured output, memory, and human-in-the-loop checkpoints — not one-shot prompts

Integrate AI agents with internal systems, external data sources, and third-party APIs to close the loop between insight and action

AI-Powered Document & Data Intelligence
Mortgage processing is document-intensive. You'll build the AI layer that makes documents machine-readable and actionable:
Extend and improve document extraction pipelines — income documents, appraisals, title, and more — using the latest extraction and classification models

Build retrieval-augmented systems that let underwriters and borrowers query complex loan files in natural language

Design feedback mechanisms that use reviewer corrections to improve extraction accuracy over time

Borrower & Loan Officer Experience
You'll use AI to make the borrower journey faster, clearer, and less stressful — and give loan officers AI-powered tools that make them dramatically more effective:
Build AI-generated task lists and smart checklists that adapt to each borrower's loan scenario

Create intelligent routing and prioritization — which loan needs attention now, which borrower is at risk of drop-off, which condition is the blocker

Design conversational AI experiences that guide borrowers through complex decisions without requiring a loan officer on every interaction

AI Evaluation & Quality Systems
You'll care about whether the AI actually works — not just whether it runs:
Build evaluation frameworks that measure AI output quality against ground truth, not just latency

Design human review workflows where AI confidence is low, and use those reviews to create labeled training data

Monitor production AI performance — accuracy, coverage, failure modes — and iterate relentlessly

What We're Looking For
Must-Have
AI systems builder: You've shipped LLM-powered features or agents to production. You've dealt with the real problems — latency, hallucination, evaluation, prompt regression, retrieval quality — not just the demos.

Strong Python: You write clean, production-quality Python. You're comfortable with async, APIs, data structures, and the engineering discipline that real systems require.

LLM tooling fluency: You've worked with the major LLM APIs and at least one agent orchestration framework. You know what structured output, tool calling, RAG, and evals look like in practice.

Product thinking: You can go from "here's a business process" to "here's an AI system design" without needing someone to translate. You think about the user, not just the model.

Ownership without guardrails: You drive features end to end — requirements, design, implementation, deployment, monitoring. You don't hand off to other teams at the hard parts.

Fast iteration: You ship early, measure, and improve. You're energized by short feedback loops and impatient with unnecessary process.

Strong Preferences
Experience in fintech, lending, insurance, or another regulated industry

Full-stack experience — comfortable building lightweight UIs, APIs, or internal tools when needed

Cloud deployment experience (AWS preferred) — you can containerize, deploy, and operate the systems you build

Experience with document understanding — OCR, extraction models, structured data from unstructured sources

Background in building human-in-the-loop systems — review workflows, feedback capture, and data flywheel design

Nice-to-Have
Experience with mortgage or real estate data

Familiarity with workflow automation tools (n8n or similar)

Exposure to fine-tuning or model adaptation for domain-specific tasks

Experience building AI products that handle PII and navigate regulatory requirements (FCRA, GLBA)

Why Saaf
Mortgage AI is still wide open. Unlike ad tech or e-commerce, where AI optimization is a rounding error, in mortgage lending an AI system that works changes whether a family gets their home. The problems are complex, the data is rich, and the solutions don't exist yet. You'll build them.
You'll push the limits of what AI agents can actually do. Not summarization. Not chatbots. Real agents navigating real loan files, making real recommendations, running complex multi-step workflows across multiple systems. The kind of AI engineering that makes you a better engineer.
AI-first means AI-first. Every engineer here uses AI to build AI. Claude Code, agentic workflows, AI-assisted code review — we don't add AI to our process, AI is our process. The team you'll join is already operating at the frontier.
Direct impact on a $2 trillion industry. Your work will touch every loan we process. When you make underwriting faster, thousands of borrowers get answers sooner. When you improve document extraction, loan officers spend less time on paperwork and more time helping people.
Compensation & Benefits
Competitive compensation

Unlimited PTO

Remote-first with flexible hours

$2,000/year professional development budget

Home office setup stipend

Originally posted on Himalayas

Interview prep

Walk in with sharper answers.

Use this as a quick practice sheet before you speak with the employer.

Role
Technology & IT Python Remote Collaboration Forward Deployed Engineer remote

Likely questions

  1. Tell us about work you have done that is close to the Forward-Deployed AI Engineer role.
  2. How would you approach your first 30 days at Saaf Finance?
  3. Which of Python, Remote Collaboration and Forward have you used recently, and what did it help you achieve?
  4. Describe a time you solved a problem without waiting to be told exactly what to do.
  5. How do you stay organised and communicate clearly when working remotely?

Prepare before the call

  • A recent example that proves your experience with Python, Remote Collaboration and Forward.
  • One short story with a problem, your action, and the result.
  • Two examples that show the strengths listed on your CV.
  • A clear reason why this role and company interest you.
  • Your availability, preferred work style, and salary expectations.

Ask them

  • What would success look like in the first 90 days?
  • What are the main problems this hire should help solve?
  • How does the team give feedback and measure good work?
  • What does a normal working week look like for this role?
Practice line

I am interested in the Forward-Deployed AI Engineer role because I can bring practical experience in Python, Remote Collaboration and Forward, learn the team quickly, and contribute to the outcomes Saaf Finance needs from this hire.

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