Who We Are
Maxwell is a mortgage technology and fulfillment company on a mission to make lending simpler, faster, and more accessible. Our platform powers hundreds of lending
institutions across the country, from independent mortgage banks and credit unions to community banks. Our mortgage Point of Sale is our flagship product, supported by loan origination, document intelligence, and private-label fulfillment capabilities. We are a remote-first team that takes craft seriously and moves with intention.

We are building a cutting-edge AI company in mortgage technology. Our document intelligence capabilities are live in production today. This role exists to accelerate what
we have and ship the next generation of AI and agentic systems.

Who You Are
You are an applied AI engineer who ships. You have taken LLM-based systems and agentic workflows from prototype to production, and you know what that requires: evals, observability, cost management, latency tuning, error handling, and iteration.

You have opinions about agent frameworks and architectures, built from what you have shipped. You are also pragmatic enough to pick the right tool for the job rather than force a favorite into every problem. You read new research, try new approaches, and form your own view on what holds up in production.

You work across the stack. You can design a retrieval system, wire up tool calls, build an eval harness, debug a production outage, and explain your choices to a product manager or an executive in the same day.

What You Will Own
Your scope from day one includes:

Design and delivery of production AI and agentic systems across document intelligence, workflow automation, and copilots
Architecture decisions for LLM-based systems, including retrieval, tool use, orchestration, memory, and evaluation
Evals and observability for production AI. You own how we know the system is working and how we catch it when it is not
Cost and latency management at production volume
Partnership with AI product on scoping and sequencing features
Partnership with data engineering on the pipelines, schemas, and data quality your systems depend on
Technical mentorship of other engineers working on AI-adjacent systems
Vendor and model evaluation, including POCs, benchmarks, and cost-performance tradeoffs

Must Haves

6+ years of software engineering experience, with at least 2 years focused on shipping production LLM-based or ML systems.
Demonstrated experience building and deploying agentic systems, including tool use, orchestration, and multi-step workflows.
Strong Python proficiency, including production-grade code quality, testing, and deployment practices.
Hands-on experience with LLM APIs (Anthropic, OpenAI, AWS Bedrock, or similar), including prompt design, structured outputs, and function calling
Production experience with evals and observability for LLM systems. You know how to measure accuracy, detect regressions, and monitor drift.
Experience with retrieval systems (RAG), vector databases, and embedding models.
Fluency with cloud infrastructure (AWS preferred), including serverless, containers, and API design.
Clear written and verbal communication skills with the ability to write design docs,.explain tradeoffs, and collaborate with product and engineering peers.
Ownership mindset - Ships work end-to-end rather than handing off.Nice to Haves

Nice to Haves

Fintech, mortgage, or regulated industry experience
Experience with document AI, OCR pipelines, or structured extraction workflows
Familiarity with AWS Bedrock, SageMaker, or equivalent cloud AI services
Experience with modern agent frameworks (LangGraph, CrewAI, AutoGen, custom) and a clear point of view on when each is appropriate
Experience with modern data warehouses (Snowflake, BigQuery) and transformation tools (dbt)
Contributions to open source AI or ML projects
Experience operating production systems on-call

What Success Looks Like

Within 60 days, you have shipped a meaningful improvement to an existing AI capability or a new one into production
Within 90 days, evals and observability are in place for the systems you own
AI systems run reliably at production volume with predictable cost and latency
Other engineers on the team ship AI work faster and with higher quality because of the patterns and tooling you have established
You are the person product and engineering leadership trust to give a straight answer on what is shippable, what is not, and what it will take

Salary Band: $180,000 – $210,000 (depending on experience and location)

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