Senior AI Machine Learning Engineer

Primary Responsibilities

• Lead day-to-day engineering execution for the EB predictive model portfolio, including pricing and underwriting models, scoring pipelines, model refreshes, monitoring, data validations, and production support.
• Build, deploy, and maintain AI/ML components and data pipelines that support applied AI use cases across pricing, underwriting, sales, service, renewal, and policy lifecycle workflows.
• Implement approved solution designs from senior Applied AI Engineers, Architects, and Data Scientists; translate design patterns into tested, reliable production code and workflows.
• Support the initial build-out of generative AI and agentic AI solutions, including prompt orchestration, retrieval-augmented generation patterns, evaluation workflows, guardrails, and integration with existing EB data and application ecosystems.
• Develop and operate batch and near-real-time data/AI pipelines for model training, feature generation, inference, post-processing, business rules integration, and downstream consumption.
• Deploy and sustain production AI services, jobs, APIs, and workflows in AWS and GCP environments using approved CI/CD, testing, observability, security, and operational practices.
• Own implementation quality for assigned components, including code reviews, unit/integration testing, documentation, runbooks, production readiness checks, and incident response support.
• Guide and mentor junior engineers by breaking down technical work, reviewing code, explaining model/data pipeline patterns, and ensuring consistent engineering practices.
• Partner with Data Scientists, Data Engineers, Asset Owners, Underwriting, Pricing stakeholders to understand requirements, validate outputs, resolve data issues, and ensure model solutions fit business workflows.
• Maintain model and pipeline governance artifacts, including lineage, model inputs/outputs, monitoring metrics, validation evidence, operational controls, and handoff documentation.
• Identify risks, bottlenecks, and operational gaps in deployed AI/ML solutions and recommend practical improvements under the guidance of senior technical leadership.

Minimum Requirements

• Bachelor’s degree in related field or 6+ years of equivalent experience in software engineering, data engineering, ML/DevOps engineering, applied AI engineering, or closely related technical roles.
• Strong hands-on expertise in Python, SQL, SDLC practices, Git-based development, automated testing, and production-grade code delivery.
• Experience deploying and operating data, AI, or ML workloads in AWS and GCP, including cloud storage, managed compute, orchestration, IAM-aware access patterns, logging, and monitoring.
• Experience with ML engineering concepts such as feature pipelines, model training workflows, batch scoring, inference services, model monitoring, drift detection, validation, retraining, and production support.
• Ability to work within defined architecture, enterprise security standards, data governance expectations, coding standards, and operational controls.
• Ability to lead implementation work, guide junior engineers, communicate tradeoffs, and manage multiple model/pipeline deliverables with limited day-to-day direction.