Senior AI Engineer

Description

The team of propensity modelers is moving to a modern, agile pod model, and this senior role carries the technical execution of that change.

You will work at the key points of the customer lifecycle and high-impact trigger events, building advanced machine learning support that enables fine-grained micro segmentation and personalization for marketers and customer journeys, with the goal of reducing churn in the most important customer segments.

You solve complex analytical problems with little guidance, have a record of running high-priority machine learning projects, and work closely with business partners on data-driven customer experiences that affect the bottom line.

Responsibilities

• Lead the design, build, and improvement of agentic AI, LLM, and machine learning and deep learning solutions.
• Build, train, and deploy the most complex, high-priority propensity models that predict customer behavior, churn risk, and lifecycle triggers.
• Own end-to-end model pipelines on your own, from advanced feature engineering through production deployment and monitoring.
• Own the full lifecycle of models used in high-impact areas, keeping them accurate, continuously improved, and tied to measurable business results.
• Act as the subject matter expert on agentic development, machine learning, and predictive modeling, guiding and mentoring junior data scientists.
• Present complex model results, performance metrics, and strategic recommendations clearly to senior leaders and cross-functional business partners.

Requirements

• A bachelor's degree, or 4+ years of work experience in its place.
• 4+ years of relevant experience, gained through work, military service, or both.
• Experience leading high-priority or high-visibility technical initiatives in a corporate setting.
• Working knowledge of LLM agent architecture, including Google ADK, LangChain/LangGraph, multi-agent orchestration, retrieval-augmented generation (RAG), MCP, and A2A protocols.
• Experience using Python, R, and SQL for advanced statistical modeling and large-scale data extraction.
• Strong grasp of machine learning and deep learning algorithms, including Transformers, transfer learning, and NLP, with hands-on use of PyTorch and scikit-learn.
• Even better if you have one or more of the following:
• Even better: a master's degree.
• Even better: experience with retrieval-augmented generation (RAG), ONNX-based embeddings, vector databases, and semantic search.
• Even better: knowledge of cloud-scale data engineering, including BigQuery pipelines and GCP services such as Cloud Run and Vertex AI.
• Even better: experience with a cloud platform (GCP, AWS, or Azure) and production MLOps tools for tracking and maintaining model workflows.
• Even better: experience building, evaluating, and deploying end-to-end propensity models, such as likelihood-to-buy, churn prediction, lead scoring, or cross-sell, using machine learning algorithms.
• You are encouraged to apply even if you do not meet every 'even better' qualification.

Conditions

This is a hybrid role with a set work location: you work from home and are in the office at least three days per week, with the schedule set by your manager.

You are responsible for following the hybrid work policies.

The role is scheduled for 40 hours per week.

The company is an equal opportunity employer.

Benefits

• Health and wellness coverage includes medical, dental, and vision, 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 paid company holidays and up to 6 personal days per year, paid parental leave, adoption assistance, and tuition assistance, plus other incentives.
• New hires receive up to 15 days of vacation per year, which increases with service.
• Depending on the role, you may earn premium pay such as overtime, shift differential, holiday pay, and allowances.
• Part-time employees may be eligible for some of these benefits, depending on their individual circumstances.
• Pay varies by location and by confirmed job-related skills and experience. The role is incentive-based, with the potential to earn more. Part-time pay is adjusted to hours.
• The annual full-time salary range for the listed location(s) is $101,000.00 - $194,000.00.