Applied AI Engineer

Job Title

Applied AI Engineer

The Applied AI Engineer is a hands‑on builder who sits at the intersection of AI engineering, operational analytics, and business process expertise. This role designs, prototypes, and operationalizes AI and analytics solutions that automate work, sharpen decision‑making, and measurably improve performance across the organization.

Embedded directly with operational leaders, the Applied AI Engineer translates real‑world business challenges into working proofs of concept — independently building end‑to‑end, including the data, modeling, and lightweight infrastructure needed to demonstrate value quickly. When solutions prove valuable, this role partners with the data engineering team to harden, scale, and operationalize them for production use.

Reporting to the Director of Advanced Analytics & AI, this position is a critical execution role within the centralized Advanced Analytics & AI team. Success requires equal fluency in modern AI tooling (including machine learning, NLP, and generative AI), rigorous analytical thinking, and the operational context needed to ensure solutions are trusted, adopted, and continuously improved. The ideal candidate combines technical depth with the business fluency needed to work effectively across stakeholders, data, and code.

The Applied AI Engineer is a hands‑on builder who sits at the intersection of AI engineering, operational analytics, and business process expertise. This role designs, prototypes, and operationalizes AI and analytics solutions that automate work, sharpen decision‑making, and measurably improve performance across the organization. The Applied AI Engineer embeds directly with operational leaders to translate real‑world business challenges into working proofs of concept, and partners with data engineering to harden and scale the solutions that prove valuable.

Reporting to the Director of Advanced Analytics & AI, this position is a critical execution role within the centralized Advanced Analytics & AI team. Success requires the ability to move quickly and independently during the prototype phase — building end‑to‑end without waiting for platform support — while also collaborating effectively with data engineering, operations, and business stakeholders to take proven solutions into production.

Equal fluency in modern AI tooling, rigorous analytical thinking, and operational context is essential.

• AI Solution Development – Design, build, and iterate on applied AI and machine learning solutions — including forecasting, classification, anomaly detection, NLP, and generative AI / LLM‑based workflows — with a focus on solving concrete operational problems.

• Rapid Prototyping & Proof of Concept – Independently build end‑to‑end POCs to validate AI and analytics ideas quickly — including standing up the data, modeling, and lightweight infrastructure needed to demonstrate value before committing to production investment.

• Path to Production – Partner with the data engineering team to harden, scale, and operationalize solutions that prove out: integrating them into operational systems, ensuring they are reliable and observable, and supporting iteration once deployed. Data engineering owns the platform and production pipelines; this role owns the AI/analytics solution running on them.

• Evaluation & Measurement – Define success metrics, design offline and online evaluations, and quantify business impact. Build the feedback loops needed to detect drift, regression, or misuse and respond to them.

• Operational Analytics & Insight – Analyze operational data, workflows, and performance trends to identify where AI and automation can deliver measurable value, and to surface actionable insights that support service delivery and efficiency.

• Embedded Business Partnership – Work directly with Geography or Vertical leadership and frontline operators to understand workflows, decision points, and constraints. Translate operational problems into well‑scoped AI and analytics solutions — and translate technical results back into clear, actionable guidance.

• Data…