Sr Data Architect

Bank of America•Charlotte, NC

22h•Onsite

About The Position

At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day. Being a Great Place to Work is core to how we drive Responsible Growth. This includes our commitment to being an inclusive workplace, attracting and developing exceptional talent, supporting our teammates’ physical, emotional, and financial wellness, recognizing and rewarding performance, and how we make an impact in the communities we serve. Bank of America is committed to an in-office culture with specific requirements for office-based attendance and which allows for an appropriate level of flexibility for our teammates and businesses based on role-specific considerations. At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us! Job Description: This job is responsible for the execution of data architectural solutions for complex initiatives that span multiple Lines of Business and control functions. Key responsibilities include facilitating solution driven discussions, working with stakeholders to support adherence to the enterprise data management policy and standards, and supporting architecture design reviews to ensure integration of data architecture principles in technology solutions. Job expectations include educating data management teams on enterprise data architectural principles and data management processes and routines. We are seeking an experienced Sr. Data Architect with deep expertise in AI Controls, Responsible AI, & enterprise data governance to architect secure, compliant, and trustworthy AI ecosystems. This senior role is responsible for designing technical guardrails, assessing AI control capabilities across platforms and tools, and ensuring the safe adoption of AI technologies enterprise‑wide. The ideal candidate combines advanced data/AI architecture experience with an expert understanding of AI risks, model governance, & the technical control frameworks required in regulated environments.

Requirements

10+ years in data architecture, ML/AI engineering, or enterprise architecture roles.
Demonstrated experience assessing AI control capabilities, including evaluating vendors, platforms, and internal systems.
Deep expertise in AI/ML governance, risk management, and Responsible AI frameworks.
Strong knowledge of enterprise data architecture, governance, lineage, metadata, and privacy controls.
Hands‑on experience with modern data platforms (Databricks, Snowflake, Azure Data Lake, Synapse, etc.).
Proficiency with AI infrastructure (vector databases, LLM orchestration platforms, embeddings pipelines).
Strong programming and data engineering skills (Python, SQL, Spark).
Experience architecting secure cloud-native solutions (Azure preferred).

Nice To Haves

Experience in regulated industries (financial services, healthcare, insurance).
Prior involvement with model risk governance or AI ethics programs.
Certifications in cloud architecture, data engineering, or AI governance.
Experience designing RAG architectures, LLMOps workflows, and GenAI guardrail frameworks.

Responsibilities

Partners with various technology teams to establish data solutions and architecture for large, complex initiatives that align with the enterprise data architecture strategy and strategic technology and platform decisions
Develops clear and concise responses to questions from senior management and control partners regarding the enterprise data architecture strategy
Manages multiple priorities in a matrixed environment with attention to detail and accuracy
Communicates effectively to influence agreement between partners and escalates items, as needed, to ensure execution activities remain on track
Ensures all relevant risk, financial, and compliance policies and standards are met
Manages relationships with business and technology partners and creates an inclusive and healthy working environment to resolve organizational impediments and blockers
Educates data management teams on enterprise data architectural principles, processes, and routines
AI Controls Architecture & Governance Design & maintain the architectural framework for AI/ML controls, ensuring alignment with enterprise risk & compliance requirements.
Establish standardized control patterns for data protection, access management, content filtering, prompt governance, and safeguard monitoring.
Evaluate & implement tools for explainability, interpretability, red‑teaming, bias detection, drift monitoring, and auditability.
Partner with Risk, Compliance, Model Risk Management, and InfoSec to harmonize AI architectural controls with enterprise governance frameworks.
Assessment of AI Control Capabilities Conduct formal assessments of AI platforms, tools, and frameworks to determine their control maturity, gaps, and alignment with enterprise standards.
Evaluate vendor AI capabilities (e.g., model APIs, LLM platforms, vector databases, AI orchestration tools) against security, privacy, and operational control requirements.
Lead control readiness evaluations for new AI solutions, including RAG pipelines, agents, LLM components, and MLOps/LLMOps platforms.
Develop structured assessment criteria for AI guardrails such as: Data governance controls Model monitoring & explainability tooling Hallucination mitigation Prompt/response filtering Access and identity management Logging, auditability, & model lineage
Provide recommendations, risk mitigation strategies, and architectural guidance based on assessment outcomes.
Responsible AI, Model Controls & Lifecycle Governance Embed Responsible AI principles (explainability, fairness, transparency, robustness) into all AI architectural designs.
Architect the full model lifecycle with built-in controls, including documentation, validation, approvals, testing, monitoring, and retirement.
Develop controlled environments for training and deploying models, including versioning, lineage, reproducibility, isolation, and audit trails.
Implement continuous monitoring frameworks for compliance, drift, bias, hallucinations, and performance degradation.
Leadership & Influence Serve as the enterprise expert for AI controls, helping shape policy, standards, and long-term architecture strategy.
Influence and guide senior leaders, engineers, and data scientists toward controlled, compliant AI adoption.