Senior Engineer-GenAI Platform Engineering

Position Summary

This is a senior platform engineering role responsible for architecting, building, and advancing Bank of America’s enterprise-scale Generative AI leveraging Data Science, Event Platform, Data Quality, Metadata, and Data Platform capabilities.

The role will help define the strategy, architecture, and engineering standards for next-generation AI and data platforms that enable self-service, governed, and scalable solutions across Consumer, Banking, Wealth, and Enterprise organizations.

The successful candidate will lead the design and delivery of reusable enterprise platform services that accelerate AI adoption, data-driven decision making, advanced analytics, agentic workflows, and digital transformation initiatives.

Key Responsibilities

• Ensures that the design and engineering approach for complex features are consistent with the larger portfolio solution
• Defines the technology tool stack for the solution and evaluates and adapts new testing tool/framework/practices for team(s)
• Enables team(s)/applications with Continuous Integration/Continuous Development (CI/CD) capabilities and engages with other technical stakeholders pertaining to efficient functioning of CI-CD pipeline
• Guides and influences team(s) on design and best practices for high code performance – e.g. pairing, code reviews
• Provides end-to-end delivery of complex features, including automation, for either a single team or multiple teams, at the program level
• Conducts research, design prototyping and other exploration activities such as evaluating new toolsets and components for release management, CI/CD, and features
• Works with stakeholders to establish high-level solution needs and with architects for technical requirements
• Provides technical leadership, architectural direction, and platform engineering expertise across enterprise Generative AI, Data Science, Data Engineering, Event Streaming, Metadata, and Data Quality initiatives
• Designs and develops reusable platform services that enable end-to-end self-service AI and data workflows, including data onboarding, data preparation, experimentation, model development, evaluation, deployment, monitoring, governance, and observability
• Defines and implements enterprise standards, reference architectures, and engineering best practices for scalable AI and data platforms
• Led the design and delivery of agentic AI applications, intelligent workflows, MCP-enabled services, and event-driven architectures using modern open-source technologies
• Pairs with business, product, architecture, and engineering teams to gather requirements, evaluate technologies, prototype solutions, and accelerate innovation
• Drives platform modernization initiatives leveraging cloud-native architectures, Kubernetes, containers, serverless services, distributed computing, and modern data processing frameworks
• Ensures platform solutions meet enterprise requirements for security, governance, resiliency, scalability, availability, compliance, and operational excellence
• Establishes and enforces CI/CD, Infrastructure-as-Code, automation, and DevSecOps practices to improve developer productivity and platform reliability
• Owns critical technology decisions and communicates architectural direction effectively to technical and executive stakeholders

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related technical discipline
• 10+ years of hands-on experience designing and building enterprise-scale AI, Data Science, Data Engineering, Metadata, Data Quality, and Analytics platforms
• Proven experience architecting and implementing enterprise Generative AI platforms, including LLM integration, agent frameworks, retrieval systems, prompt orchestration, vector-enabled architectures, and AI governance capabilities
• Strong experience building self-service platforms supporting the complete AI/ML lifecycle, including data ingestion, feature engineering, experimentation, model development, deployment, inferencing, monitoring, and observability
• Deep understanding of modern AI and data platform architectures, including storage-compute separation, distributed processing, interactive development environments, containerization, and developer productivity tooling
• Experience designing and implementing metadata-driven platforms, semantic layers, data lineage, data quality frameworks, knowledge graphs, and enterprise data governance solutions
• Hands-on expertise with Python and modern AI/ML ecosystems, including open-source frameworks, libraries, and model-serving technologies
• Experience building and deploying scalable AI and data workloads on Kubernetes, containers, virtualized infrastructure, and cloud-native environments
• Practical experience developing enterprise-grade APIs, microservices, and distributed systems supporting high-volume data and AI workloads
• Experience implementing CI/CD, automated testing, infrastructure automation, and DevSecOps practices using enterprise toolchains
• Strong understanding of platform observability, monitoring, security, governance, reliability, recoverability, and operational excellence
• Proven ability to collaborate with cross-functional teams, influence architectural decisions, and communicate complex technical concepts to diverse audiences

Preferred Qualifications

• Experience building enterprise-wide GenAI ecosystems including AI gateways, model management, prompt management, vector databases, agent orchestration, and responsible AI capabilities
• Experience with Retrieval-Augmented Generation (RAG), agentic architectures, MCP servers, AI workflow orchestration, and enterprise knowledge platforms
• Strong knowledge of cloud-native AI and data platforms across public and private cloud environments
• Experience with developer platforms, internal AI copilots, self-service data products, and platform product management
• Experience establishing enterprise AI governance, compliance, risk controls, and responsible AI practices
• Demonstrated success leading large-scale platform transformations and modernizing legacy analytics and data science ecosystems

Skills

• Automation
• Influence
• Result Orientation
• Stakeholder Management
• Technical Strategy Development
• Application Development
• Architecture
• Business Acumen
• Risk Management
• Solution Design
• Agile Practices
• Analytical Thinking
• Collaboration
• Data Management
• Solution Delivery Process

Hours Per Week: 40