Responsibilities

We're looking for a rare, full-stack data and AI practitioner who can operate fluidly from raw data all the way to production AI systems and the enterprise architecture that supports them. You will ingest and reason over everything from highly structured warehouse data in Snowflake to messy unstructured sources, turn it into defensible analytics, and ship agentic AI solutions that are both intelligent and ruthlessly cost-efficient.
This is a builder-first role with real architectural ownership. You'll write the models and the agents yourself, and you'll define the reference architectures, patterns, and standards that let the rest of the organization build on top of your work. If you're equally comfortable defending a confidence interval and a system-design decision, this role is for you.
Unify structured and unstructured data. Build pipelines that pull structured data from Snowflake (and adjacent warehouses/lakes) alongside unstructured sources — text, documents, logs, transcripts — into clean, modeling-ready datasets.
Deliver decision-grade analytics. Produce customer churn analytics with properly quantified uncertainty (confidence/credible intervals), not just point estimates, and communicate what the numbers can and can't support.
Build predictive and prescriptive models. Move beyond "what will happen" to "what should we do about it" — forecasting, propensity, and optimization/recommendation systems that drive concrete business actions.
Engineer agentic AI systems. Design and ship LLM-powered agents and workflows that are token-efficient by design — tight context management, retrieval and caching strategies, model routing, and evaluation harnesses that keep cost and latency low without sacrificing quality.
Architect for the enterprise. Define reference architectures, integration patterns, and governance standards spanning data ingestion, model development, MLOps/LLMOps, security, and observability — and bring stakeholders along with clear diagrams and documentation.
Own quality and reliability. Establish evaluation, monitoring, and guardrails for drift, accuracy, bias, safety, and cost across both classical ML and GenAI systems.
Partner across the business. Translate ambiguous business problems into technical solutions and explain technical tradeoffs to non-technical stakeholders.
What You Bring (Required)
Strong hands-on data engineering with Snowflake (modeling, performance, cost management) and SQL, plus experience wrangling unstructured data.
Solid applied statistics: you can build churn/retention models and correctly express uncertainty with confidence or credible intervals, and you understand the assumptions behind them.

Staff Applied AI Engineer at A3c41b8b71eff8c4 · LumiJobs

Staff Applied AI Engineer

Responsibilities

Qualification

Required

Preferred

Benefits

Company

A3c41b8b71eff8c4

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