Senior Engineer-GenAI Platform Engineering

Bank of America•Pennington, AL

6d•$122,000 - $200,000•Onsite

About The Position

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. This individual will work closely with architects, product owners, engineers, data scientists, and business stakeholders to build highly scalable, secure, and resilient platforms leveraging cloud-native technologies, distributed computing, modern data architectures, and Generative AI frameworks. The ideal candidate combines deep technical expertise with strong leadership skills, a passion for innovation, and the ability to translate complex business needs into enterprise-grade platform capabilities. This job is responsible for defining and leading the engineering approach for complex features to deliver significant business outcomes. Key responsibilities of the job include delivering complex features and technology, enabling development efficiencies, providing technical thought leadership based on conducting multiple software implementations, and applying both depth and breadth in a number of technical competencies. Additionally, this job is accountable for end-to-end solution design and delivery.

Requirements

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.
Strong experience designing event-driven and streaming architectures using technologies such as Kafka, Apache Spark, Flink, or equivalent distributed processing frameworks.
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.

Nice To Haves

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.

Responsibilities

Ensures that the design and engineering approach for complex features are consistent with the larger portfolio solution
Define the technology tool stack for the solution and evaluate and adapt 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
Provide technical leadership, architectural direction, and platform engineering expertise across enterprise Generative AI, Data Science, Data Engineering, Event Streaming, Metadata, and Data Quality initiatives.
Design and develop 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.
Define and implement enterprise standards, reference architectures, and engineering best practices for scalable AI and data platforms.
Lead the design and delivery of agentic AI applications, intelligent workflows, MCP-enabled services, and event-driven architectures using modern open-source technologies.
Partner with business, product, architecture, and engineering teams to gather requirements, evaluate technologies, prototype solutions, and accelerate innovation.
Drive platform modernization initiatives leveraging cloud-native architectures, Kubernetes, containers, serverless services, distributed computing, and modern data processing frameworks.
Ensure platform solutions meet enterprise requirements for security, governance, resiliency, scalability, availability, compliance, and operational excellence.
Establish and enforce CI/CD, Infrastructure-as-Code, automation, and DevSecOps practices to improve developer productivity and platform reliability.
Provide technical mentorship, conduct architecture reviews, perform code reviews, and champion engineering excellence across teams.
Own critical technology decisions and communicate architectural direction effectively to technical and executive stakeholders.