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.

Data & Analytics Engineering Alignment

• Work with large-scale structured and unstructured data to support AI/ML development and evaluation.
• Define data requirements and partner with data engineering teams to improve data quality, availability, lineage, and observability.
• Develop AI/ML-specific data transformations and features while partnering with data engineers on shared, governed, production-grade pipelines.
• Contribute to curated data assets and integrate AI capabilities with enterprise analytics, business intelligence, and operational systems.

Governance, Standards & Responsible AI

• Ensure adherence to enterprise AI governance, security, and compliance standards
• Apply responsible AI practices, including documentation, explainability, and risk considerations
• Contribute to development and refinement of AI/ML best practices and reusable patterns
• Support model documentation and auditability requirements

Business Partnership & Value Realization

• Partner with stakeholders to convert ambiguous business problems into testable hypotheses, technical requirements, and delivery plans.
• Define technical and business success measures with product owners and business partners.
• Assess whether AI/ML is appropriate for a use case and recommend conventional analytics, rules-based, or software approaches when they offer a better solution.
• Estimate delivery complexity, dependencies, operational impact, and technical risk.

Technical Leadership & Collaboration

• Serve as technical lead for medium-to-large AI/ML initiatives and drive resolution of complex implementation issues.
• Mentor junior team members
• Review code, models, technical designs, and operational plans to improve quality and consistency.

Minimum Requirements

• Bachelor's degree and at least 2 years of software engineer experience

OR

• Master's degree and at least 1 year of software engineer experience

OR

• At least 5 years of software engineer experience

Additional Requirements

• Qualified candidates, excluding current employees, must be legally authorized on an unrestricted basis (US Citizen, Legal Permanent Resident, Refugee or Asylee) to be employed in the United States. We do not anticipate providing employment related work sponsorship for this position (e.g., H-1B status)

Desired Skills

• Master’s degree in a quantitative or technical field and preferred 5 years of relevant experience working as either a Data Scientist, Data Engineering, Machine Learning Engineer, or AI engineer
• Experience with production AI/ML solution delivery
• Preferred 7 years of relevant professional experience, including demonstrated ownership of production AI/ML systems
• Demonstrated experience developing maintainable Python software and deploying AI/ML solutions into production environments
• Experience developing tested, maintainable Python applications and using common machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch.
• Experience with model deployment, model serving, CI/CD, experiment tracking, versioning, monitoring, and retraining workflows.
• Experience with cloud-based AI/ML platforms, containerized deployments, APIs, distributed data processing, and enterprise data ecosystems.
• Experience designing or operating generative AI solutions, including retrieval-augmented generation and systematic evaluation.
• Familiarity with enterprise architecture, cybersecurity, privacy, responsible AI, and model governance practices.
• Experience working across business, product, data, engineering, architecture, and risk teams in an enterprise environment.