Senior AI Engineer

Description

The company is building a new agile pod model for its propensity modeling work, and this Senior AI Engineer will lead the technical side of that move.

You will be part of the propensity modeling team and work at key points in the customer lifecycle and at high-impact trigger events.

Your machine learning work will support fine-grained microsegmentation and personalization, help marketers, improve customer journeys, and reduce churn in the company's most important customer segments.

Responsibilities

• Lead the design, development, and tuning of agentic AI, LLM, and machine learning and deep learning solutions.
• Build, train, and deploy the highest-priority propensity models that predict customer behavior, churn risk, and lifecycle triggers.
• Consolidate overlapping propensity models into a shared customer state and unified models to cut operating and development costs.
• Work independently on full model pipelines, from advanced feature engineering through production deployment and monitoring.
• Act as the subject matter expert on agentic development, machine learning, and predictive modeling, and mentor junior data scientists.
• Own models in high-impact areas, keeping them accurate, continuously improved, and delivering measurable business results.
• Present model insights, performance metrics, and strategic recommendations clearly to senior leaders and cross-functional business partners.

Requirements

• You enjoy solving complex analytical problems with little guidance and have a record of leading high-priority machine learning projects.
• You work well with business partners to build data-driven customer experiences that affect the bottom line.
• Bachelor's degree or four or more years of work experience.
• Four or more years of relevant experience, from work and/or military service.
• Experience with high-priority or high-visibility technical projects in a corporate environment.
• Experience designing and measuring A/B tests or uplift models for targeting.
• Strong skills in machine learning and deep learning, including transformers, transfer learning, NLP, PyTorch, scikit-learn, probability calibration, and time-based validation.
• Experience with Python, R, and SQL for advanced statistical modeling and large-scale data extraction.
• Experience with cloud platforms such as GCP, AWS, or Azure, and production MLOps tools for tracking and maintaining model workflows.
• Even better if you have one or more of the following:
• Knowledge of LLM agent architecture, such as Google ADK, LangChain/LangGraph, multi-agent orchestration, retrieval-augmented generation (RAG), MCP, A2A protocols, ONNX-based embeddings, vector databases, and semantic search.
• Knowledge of cloud-scale data engineering, such as BigQuery pipelines and GCP services like Cloud Run and Vertex AI.
• Apply even if you do not meet every 'even better' qualification.

Conditions

This is a hybrid role. Your work location is defined and includes working from home plus a minimum of three days per week in the office, set by your manager.

You are responsible for following the hybrid work policies.

The role is scheduled for 40 hours per week.

The annual full-time salary range for the listed location(s) is $111,500.00 - $194,000.00.

Pay depends on location and on confirmed job-related skills and experience. This is an incentive-based position with potential to earn more.

For part-time roles, pay is adjusted to match hours.

Benefits

• Health and wellness options include medical, dental, and vision coverage, short- and long-term disability, basic and supplemental life insurance, AD&D insurance, identity theft protection, pet insurance, and group home and auto insurance.
• A matched 401(k) savings plan.
• Up to 8 company-paid holidays and up to 6 personal days per year.
• Paid parental leave, adoption assistance, and tuition assistance, along with other incentives.
• Depending on the role, premium pay such as overtime, shift differential, holiday pay, and allowances.
• Up to 15 days of vacation per year for new hires, increasing with service.
• Part-time employees may qualify for some of these benefits depending on their individual circumstances.