Senior GenAI Platform Engineer

Primary Skill

Gen AI Tools

Python, Machine Learning, Data Engineering

Hadoop

12

• Kindly share Linked In IDs of candidates.
• Client requires passport number of candidates for this role.
• Candidates with Banking Financial Services background and overall 12+ Years of experience are highly preferred.

This role is a strategic platform engineering lead responsible for designing and scaling enterprise Generative AI, Data Science, Metadata, Data Quality, and Event-Driven platform capabilities within MALTS. The position will drive self-service AI enablement, platform modernization, and reusable enterprise services that accelerate AI adoption across Consumer, Banking, and Wealth businesses. Ideal candidates possess deep expertise in GenAI, platform engineering, data engineering, distributed systems, governance, and enterprise-scale AI platforms.

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.

• 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 Dev Sec Ops  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.

• 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…