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. 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. 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 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 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 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 Shift 1st shift (United States of America) Hours Per Week 40 Pay Transparency Details US - NJ - Pennington - 1500 American Blvd - Hopewell Bldg 5 (NJ2150) Pay $122,000.00 - $200,000.00 annualized salary, offers to be determined based on experience, education and skill set. Discretionary Incentive This role is eligible to participate in the annual discretionary plan. Employees are eligible for an annual discretionary award based on overall individual performance results and behaviors, the performance and contributions of their line of business and/or group, and the overall success of the Company. Benefits This role is currently benefits eligible. We provide industry‑leading benefits, access to paid time off, resources and support to our employees so they can make a genuine impact and contribute to the sustainable growth of our business and the communities we serve. #J-18808-Ljbffr Bank of America