Sr. Applied AI Engineer

Role Description

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

• 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.

Qualifications

• 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.

Requirements

• You use AI tools (Claude, Cursor, Claude Code, or equivalent) as a core part of your daily workflow.
• 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.
• 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.
• Win as a Team: Builds trust and works together by making sure everyone communicates well.
• 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.
• Innovate Boldly: We challenge the status quo and push boundaries to create meaningful change.

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.
• Named on Inc 5000 list of America's Fastest Growing Private Companies.
• Active employee-led Culture Committee.
• Ongoing industry and professional development trainings available to all employees.