AI Platform Director- Data Engineering

First Citizens Bank•Raleigh, NC

About The Position

We are seeking an experienced Director to lead the AI platform engineering and enablement functions within our expanding Cloud Data and AI Platform organization. This role is instrumental in building, operationalizing, and governing the next-generation AI and machine learning ecosystem that powers advanced analytics and responsible AI adoption across the bank. You will own the end-to-end AI lifecycle—from data and model development to MLOps, deployment, governance, and responsible AI compliance in a regulated financial environment. As a seasoned technology leader, you will bring your expertise in enterprise AI architecture, model operations, and platform engineering to partner with key business, technology, and governance stakeholders—ensuring AI initiatives are responsibly implemented, well-controlled, and deliver measurable value.

Requirements

Bachelor's Degree and 8 years of experience in Information Technology including application development, support roles, and management. OR High School Diploma or GED and 12 years of experience in Information Technology including application development, support roles, and management.
Deep hands-on experience building production ML systems on AWS
2+ years in AI/ML, data science, or data engineering leadership roles
Strong knowledge of: Machine learning (XGBoost, deep learning, NLP, time series)
Strong knowledge of: MLOps practices (CI/CD, model monitoring, drift detection)
Strong knowledge of: Distributed systems and cloud architecture
Strong programming background in Python + SQL (Scala/Java a plus)
Experience working in regulated environments with model governance

Nice To Haves

Experience with Generative AI / LLM platforms (Bedrock, OpenAI, Claude APIs)
Experience in financial services, banking, fintech, or insurance
Familiarity with data platforms like Snowflake, Databricks

Responsibilities

Architect and lead AI/ML workloads on AWS including: Amazon SageMaker (training, deployment, model registry), AWS Bedrock (foundation models and GenAI use cases), AWS Lambda, ECS, EKS for model serving, S3, Glue, Snowflake for data pipelines.
Define enterprise standards for MLOps, feature stores, and model lifecycle management.
Build and maintain integrations with enterprise platforms for data ingestion, metadata management, tokenization, and control evidence generation.
Continuously enhance the platform’s automation, resilience, and observability, ensuring robust end-to-end telemetry for both model and data pipelines.
Collaborate with Enterprise Risk, Legal, Compliance, and Model Risk partners to embed Responsible AI principles and audit-ready control evidence directly into platform design.
Oversee development of ML models across all business units including Fraud detection systems, Credit scoring and risk modeling, Customer segmentation and personalization, Liquidity related modeling etc.
Lead GenAI initiatives using LLMs for Document intelligence, AI copilots etc.
Partner with data engineering teams to ensure high-quality, governed datasets.
Define feature engineering and data product standards in Snowflake / data lake environments.
Integrate real-time streaming data for low-latency decision systems.
Define and enforce standards, patterns, and guardrails for model deployment, explainability, lineage, and monitoring in alignment with enterprise risk, compliance, and security frameworks.
Partner closely with leaders across Responsible AI Governance, AI Portfolio Management, AI Fluency & Engagement, and Applied Data Science & GenAI, in collaboration with enterprise risk partners, to implement a responsible AI framework that embeds audit-ready control evidence and governance mechanisms directly into the platform’s core design to ensure the platform supports scalable, ethical, compliant, and high-impact AI delivery.
Implement model explainability (SHAP, LIME, interpretability frameworks).
Establish responsible AI policies (bias detection, fairness, auditability).
Develop and mentor engineering talent, championing Agile practices, continuous learning, and adoption of emerging AI and data engineering technologies.
Mentor senior technical leaders and establish engineering best practices.
Oversee technical due diligence, onboarding, and management of strategic AI and GenAI vendors and tools, ensuring compatibility with enterprise architecture and control.