Jobs and Careers

Sr. Applied AI Engineer

Wilmington, United Statesfull_timeVerifiedPosted 22 May 2026

About the role

Who is Vantaca

Powered by AI, Vantaca's vision is big! We are the leading AI-native community management performance platform that enables owners and operators, community management teams, boards and associations to work smarter, faster, and with unprecedented insight. More than just accounting and management software, Vantaca is intelligent business operating software that leverages artificial intelligence to automate routine work, surface actionable insights, and help our customers increase revenue, efficiency, flexibility, and control.

Built on modern cloud architecture with a single-platform design, Vantaca combines comprehensive functionality that adapts to 100% of business processes with AI-powered automation that learns and improves over time. Our proactive AI capabilities don't just report on what happened; they predict what's coming and recommend what to do next. From intelligent document processing and predictive analytics to automated workflows and conversational interfaces, we're transforming how community management companies operate. 

With seamless integrations across the software and banking ecosystem, we're building the intelligent hub for community management where AI doesn't just assist, it anticipates. Vantaca is focused exclusively on community management and is the trusted technology leader defining the AI-powered future of the community association management industry. We're building something fundamentally different, and our customers are experiencing the competitive advantage that comes from working with truly intelligent software.

Vantaca just achieved unicorn status with a $1.25B valuation, so it's safe to say we're past the "scrappy startup phase." We're not just building a successful company, we're building the category-defining platform that will transform how an entire industry operates.

Overview

We 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