Sr. Applied AI Engineer

Who Is Vantaca Powered by AI, Vantaca is a leading AI‑native community management performance platform that enables owners, operators, community management teams, boards, and associations to work smarter, faster, and with unprecedented insight. 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. 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 retrieval‑and‑generation, 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. Technical Requirements You use AI tools (Claude, Cursor, Claude Code, or equivalent) as a core part of your daily workflow, not occasionally. 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 – Loves change and finds new ways to improve knowledge and the product. Win as a Team – Builds trust and works together by communicating well, actively involving in daily work, listening to ideas, and celebrating successes. 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 to understand needs, address concerns, and improve processes. Innovate Boldly – Challenges the status quo, pushes boundaries to create meaningful change, and acts with urgency and purpose. 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. Benefits: Medical, Dental, and Vision coverage start day one. Unlimited PTO with a requirement for employees to take a minimum of one continuous week per year. 401(k) 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. Named on Inc 5000 list of America's Fastest Growing Private Companies. Named on Inc 5000 Vet 100 Private Companies list multiple years in a row. Winner of Coastal Entrepreneur Award, Technology Category. Active employee‑led Culture Committee. Ongoing industry and professional development trainings available to all employees. Multiple leaders on the executive committee recognized as 40 under 40 recipients for contributions to business and community. We’re playing offense to win! Our product‑market fit and world‑class employees make us a leader in our space. #J-18808-Ljbffr