Senior AI/ML Engineer

Position Overview

The Senior AI/ML Engineer designs, develops, deploys, and operates production-grade artificial intelligence and machine learning solutions that deliver measurable business outcomes. This role combines machine learning expertise, software engineering discipline, and solution architecture to translate complex business needs into secure, scalable, supportable, and responsible AI capabilities.

The Senior AI/ML Engineer provides technical leadership throughout the solution lifecycle, including discovery, architecture, data preparation, model development, evaluation, deployment, monitoring, and continuous improvement. Working within a federated AI Center of Excellence (COE) model, this individual will partner with data engineering, analytics, product, architecture, cybersecurity, and business teams to develop and deliver business value through scalable AI and ML models.

Responsibilities

Model Development & Evaluation

• Design, build, validate, and deploy machine learning models for complex business use cases.

Lead feature engineering, model selection, tuning, validation, and production-readiness assessments.

• Select the simplest effective approach based on business requirements, data availability, explainability, operational constraints, and measurable performance. • Define model acceptance criteria and implement reproducible offline and production evaluations. • Assess model robustness, bias, explainability, limitations, and failure modes appropriate to the use case.

AI Solution Delivery & ML Operations

• Own end-to-end delivery of AI/ML solutions from discovery and prototyping through production deployment and ongoing operation. • Implement model and data versioning, experiment tracking, reproducible pipelines, automated deployment, and rollback practices. • Establish monitoring for data quality, data drift, model performance, reliability, latency, utilization, and cost. • Define retraining, release, environment-promotion, and lifecycle-management approaches. • Provide production support, perform root-cause analysis, and drive corrective and preventive actions for operational issues.

Generative AI Solution Engineering

• Design and operationalize generative AI solutions using fit-for-purpose techniques such as retrieval-augmented generation, prompt orchestration, tool integration, and structured output. • Implement appropriate guardrails, human oversight, content controls, and monitoring. • Evaluate foundation models and AI platforms against business, security, architecture, and governance requirements and standards.