OverviewWe are building a world-class Applied AI practice inside Vantaca's Applied AI team. We need someone who can ship production-grade ML and LLM systems for our Implementation and Client Enablement teams. This is not a prompt engineering role or an AI exploration sandbox. You will build systems that are evaluated, deployed, and observed - owning the gap between "interesting model" and "thing that reliably runs in production."
You will partner with Implementation PMs, Solution Consultants, and Client Enablement Specialists to identify the highest-leverage problems and ship tooling that removes friction across the client lifecycle. The work is high-trust and high-autonomy - you own your problem space end to end.
What you'll work on- Our Implementation and CE teams have a validated backlog of high-value AI builds - risk surfacing, workflow intelligence, client coaching, configuration assistance - and no dedicated engineering resources to execute on them. You change that.
Concretely, you'll:
- Design and ship ML and LLM systems spanning supervised models that predict and rank, retrieval and generation systems that draft and summarize, and agentic workflows that act on internal data
- Build evaluation infrastructure alongside every system - define success criteria before writing code, measure whether the system worked, and catch regressions before users do
- Architect RAG, retrieval, and context engineering patterns that let LLMs operate reliably on internal knowledge and production data
- Reason rigorously about modeling choices - label definition, leakage, time-aware splits, calibration, precision-at-k vs AUC, when a heuristic baseline beats a model
- Work directly in Databricks and Unity Catalog - understand the operational data, write the SQL, and build systems that act on it
- Own deployment and monitoring for everything you ship - feature drift, outcome tracking, LLM eval regression, retraining cadence, rollback paths
- Treat data governance and access scoping as design constraints, not afterthoughts
- Maintain versioned, traceable LLM workflows - prompts and context patterns that are reusable, not one-off
What we're looking for- Production experience shipping both classical ML and LLM systems - strong opinions on when to use which
- An eval-first mindset - you don't trust a system you haven't measured, and you build the measurement before the model
- Fluency in a data warehouse environment - SQL, time-aware feature engineering, leakage discipline
- Production scars - you've watched a model degrade in the wild, seen a label loop bias itself, caught an LLM provider regression with the prompt unchanged
- Cost intuition - you can napkin-math the unit economics of an LLM workflow before committing to it
- Ability to scope work in partnership with non-technical stakeholders, translating their pain into a buildable system
- Comfort with distinguishing the business metric from the model metric, and arguing for the right one
RequirementsTechnical Requirements
- You use AI tools (Claude, Cursor, Claude Code, or equivalent) as a core part of your daily workflow. Not occasionally. As a thinking partner and execution accelerator.
- Python proficiency as your primary build language for automation and scripting
- Full-stack range: comfortable building APIs, automations, integrations, and lightweight UIs without needing a separate front-end resource
- SQL and data fluency: you will work regularly in our data warehouse and need to understand and act on operational data directly
- API integration experience: REST, webhooks, OAuth
- RAG and retrieval system experience: chunking, embedding strategies, retrieval quality, hallucination mitigation
- Prompt and context engineering: you understand why context boundaries matter and have a strategy for what to persist vs. retrieve
- DevOps fundamentals: CI/CD, Infrastructure as Code, containerization. You ship and maintain what you build.
- AI/ML background is required; CS or AI/ML academic track preferred
Mindset and Approach
- Spec-first by default: you write detailed intent documents before building. Resistance to structured planning upfront is a disqualifier for this role.
- Bias toward shipping: you prefer a v1 in two weeks over architecting a v3 for two months
- Product sense for non-technical users: you can translate operational pain into a scoped technical solution without requiring a detailed spec from the person who has the problem
- Comfortable as the first and only: you are energized by operating as the sole AI engineer in a domain, without a peer engineering org to lean on day to day
- Builder, not buyer: you build internal tooling and do not stitch together SaaS products
- Security-aware: you ask about access scoping and data classification before you build, not after
- Comfortable with ambiguity: requirements will shift; you orient toward the outcome
- Strong written communicator: you document your intent before you build and leave clear records of what you built and why
Core Values- Always Growing: Likes change and enjoys finding new ways to improve their knowledge and the product. Always ready to learn quickly, helping themselves and the team grow.
- Win as a Team: Builds trust and works together by making sure everyone communicates well. Actively involved in daily work, working closely with the team, listening to their ideas, and celebrating successes together.
- Accountability Starts with Me: Notices problems and takes personal action to solve them.
- Unwavering Commitment to Customer Experience: Regularly talks to residents and management companies, taking personal responsibility to understand what they need, address concerns, and make their experience better with improved Vantaca processes.
- Innovate Boldly: We challenge the status quo and push boundaries to create meaningful change. We act with urgency and purpose, knowing that innovation drives our success.
Why You Should Join Our Team- Build consumer products that millions use. Shape how homeowners across the country interact with their communities every day.
- AI-First Product Culture with access to cutting-edge tools and autonomy to experiment.
- Our eNPS is +68! (Google it, that is great)
- Benefits: Medical, Dental, and Vision kick in day one
- Unlimited PTO (with a requirement for employees to take a minimum of one continuous week per year)
- 401K with Company Match
- Remote Flexible - come to the office when needed
- Great parental leave benefits
- Unicorn-stage growth: $1.25B valuation, $300M Series C, scaling from 275 to 400+ employees
