Director, AI/ML and Data Architecture

Fidelity Investments•Durham, NC

About The Position

In this leadership role, you will drive the Data Architecture and Data platform modernization for the Workplace Investing division of Fidelity Investments. You will work with Enterprise Architects to define strategies to modernize legacy systems, leveraging AI/ML‑enabled approaches for mainframe and legacy platform modernization, along with other leading‑edge initiatives. You will collaborate closely with business partners and technology teams to understand business objectives and define the appropriate architectural direction.

Requirements

BS or MS required
10+ years of experience working with data and databases
Experience architecting large, complex systems with high‑volume and high‑performance requirements
Ability to work collaboratively with Enterprise Architects across business units to create a unified Fidelity Architecture Strategy
Experience partnering with Fidelity Legal, Risk, Compliance, and Cybersecurity teams to ensure safe and secure handling of enterprise, client, and customer data in public cloud environments
Experience designing, building, and modernizing large‑scale, resilient, and high‑performance data platforms supporting both operational and analytical workloads
Ability to develop conceptual and logical data models aligned to business outcomes
Ability to define phase‑wise modernization and implementation strategies for large, multi‑year platform programs
Strong experience with data migration strategies and patterns, especially involving legacy systems and cloud‑native platforms
Deep understanding of relational and non‑relational data stores, with the ability to make and clearly articulate architectural trade‑offs
Strong knowledge of data processing, data movement, and integration patterns across batch, streaming, and event‑driven architectures
Expertise in evaluating, prototyping, and recommending technologies based on business, architectural, and operational needs
Strong understanding of resiliency, security, privacy, and cloud deployment patterns
Proven experience in data quality, large‑scale data analysis, pattern identification, and insight generation
Experience designing data stores optimized for different consumption patterns, including operational, analytical, AI/ML, and API‑driven use cases
Strong understanding of AI/ML and Generative AI concepts (such as LLMs, embeddings, and retrieval‑augmented generation), and how they are applied within modern enterprise platforms, including data pipelines, models, and AI‑enabled services
Experience using AI to modernize legacy systems, including AI‑assisted code analysis, automated documentation, and system knowledge extraction from legacy technologies (e.g., COBOL, .NET, C#), as well as modern code generation
Ability to leverage AI and development assist tools (such as spec‑driven development, AI‑assisted design reviews, and automated test case generation) to improve the software development lifecycle (SDLC), accelerating design, build, testing, and migration activities during legacy‑to‑modern transformations
Familiarity with AI‑powered engineering productivity tools, including code assistants, modernization accelerators, and automated testing and quality‑validation solutions (e.g., GitHub Copilot, Claude, Gemini)
Hands‑on enough to design and lead proof‑of‑concepts (POCs) that demonstrate the value of AI and GenAI in platform modernization, without acting as a full‑time developer
Understanding of responsible AI practices, including data governance, model risk, explainability, and compliance considerations in regulated environments
Ability to communicate how AI‑enabled modernization improves speed, quality, and scalability, and to clearly articulate value, trade‑offs, and risks to business and technology leaders
Ability to learn and adapt
Critical thinking

Nice To Haves

Experience modernizing the legacy platform

Responsibilities

Drive the Data Architecture and Data platform modernization for the Workplace Investing division.
Work with Enterprise Architects to define strategies to modernize legacy systems, leveraging AI/ML‑enabled approaches.
Collaborate closely with business partners and technology teams to understand business objectives and define the appropriate architectural direction.
Design, build, and modernize large‑scale, resilient, and high‑performance data platforms supporting both operational and analytical workloads.
Develop conceptual and logical data models aligned to business outcomes.
Define phase‑wise modernization and implementation strategies for large, multi‑year platform programs.
Lead proof‑of‑concepts (POCs) that demonstrate the value of AI and GenAI in platform modernization.
Communicate how AI‑enabled modernization improves speed, quality, and scalability, and to clearly articulate value, trade‑offs, and risks to business and technology leaders.