Senior Engineer – GenAI Platform Automation

Position Summary

This is a senior platform automation engineering role focused on accelerating enterprise adoption of Generative AI, Data Science, Data Engineering, and Advanced Analytics capabilities across Bank of America. The role will lead automation initiatives that improve developer productivity, platform reliability, operational efficiency, governance, and self-service adoption across enterprise AI and data platforms. The successful candidate will be responsible for designing, building, and operationalizing automated platform capabilities spanning infrastructure provisioning, CI/CD, environment management, governance controls, observability, testing, deployment automation, and AI workload enablement. The individual will work closely with platform engineering, cloud engineering, architecture, data science, and business teams to deliver scalable, secure, and resilient automation solutions supporting the full lifecycle of AI and analytics workloads. This role requires strong expertise in platform automation, cloud-native technologies, Infrastructure-as-Code (IaC), DevSecOps, Generative AI ecosystem tooling, and distributed computing platforms. The ideal candidate combines deep engineering expertise with a passion for automation, operational excellence, and continuous platform innovation.

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
• Lead automation initiatives for enterprise GenAI, Data Science, Metadata, Data Quality, Event Streaming, and Analytics platforms
• Design and implement self-service automation capabilities that streamline onboarding, environment provisioning, deployment, governance, monitoring, and operational workflows
• Build automated platform services supporting the complete AI and analytics lifecycle including data preparation, experimentation, model training, deployment, inferencing, observability, and lifecycle management
• Develop Infrastructure-as-Code (IaC) solutions using Terraform and related automation frameworks to enable repeatable, scalable, and compliant infrastructure deployments
• Design and implement enterprise CI/CD pipelines, automated testing frameworks, deployment automation, and release management processes using Atlassian and related DevOps toolchains
• Partner with platform engineering and cloud teams to automate Kubernetes, container, serverless, and distributed computing environments
• Build automation solutions supporting agentic AI applications, MCP-enabled services, event-driven architectures, and enterprise AI workflows
• Drive operational excellence through platform monitoring, observability, automated remediation, performance optimization, and reliability engineering practices
• Collaborate with architecture, engineering, governance, security, and business stakeholders to ensure platforms meet enterprise standards and compliance requirements
• Conduct technical design reviews, automation assessments, code reviews, and establish engineering best practices across teams
• Provide technical leadership, mentorship, and guidance to engineering teams adopting automation-first development and operational practices
• Support key business initiatives including Consumer AML Analytics and other strategic AI platform adoption efforts

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or job related field required
• 10+ years of hands-on experience in platform engineering, automation engineering, cloud engineering, DevOps, or large-scale distributed systems
• Proven experience building self-service enterprise platforms supporting AI/ML, Data Science, Data Engineering, and advanced analytics workloads
• Strong expertise in automation frameworks, DevOps methodologies, CI/CD pipelines, Infrastructure-as-Code, and software delivery lifecycle automation
• Deep understanding of modern open-source Generative AI and Data Science platform architectures including storage and compute separation, interactive development environments, virtual environments, containers, Jupyter, VSCode, and developer productivity tooling
• Hands-on experience implementing enterprise CI/CD automation using Atlassian ecosystem tools including Bitbucket, Bamboo, Jira, and Confluence
• Experience designing and implementing Infrastructure-as-Code solutions using Terraform and cloud-native automation frameworks
• Strong understanding of metadata management, data lineage, governance frameworks, and semantic layer concepts supporting enterprise AI and data platforms
• Experience building scalable cloud-native solutions utilizing distributed computing architectures and modern platform engineering principles
• Experience automating deployments and operations for Kubernetes, containerized, YARN, serverless, and distributed processing environments
• Experience designing and supporting event-driven architectures leveraging technologies such as Kafka and streaming data platforms
• Working knowledge of agentic AI architectures, MCP frameworks, API integrations, workflow automation, and enterprise AI enablement platforms
• Strong Python development experience for automation, orchestration, scripting, tooling, and operational engineering use cases
• Knowledge of cloud engineering principles including networking, infrastructure management, security, resilience, scalability, and cost optimization
• Experience implementing observability frameworks including logging, monitoring, tracing, alerting, automation, and operational dashboards
• Ability to communicate effectively with engineers, architects, product owners, and business stakeholders across varying

Desired Qualifications

• Experience supporting enterprise Generative AI platforms, AI governance frameworks, model management, and AI operationalization initiatives
• Knowledge of AML, financial crime, risk analytics, fraud detection, or banking domain platforms
• Experience building platform automation for data governance, data quality, metadata management, and model lifecycle management
• Experience implementing GitOps, DevSecOps, Reliability Engineering (RE), and platform engineering best practices
• Familiarity with large-scale cloud environments and enterprise data platforms
• Experience creating reusable developer platforms, internal engineering tools, and self-service automation capabilities at enterprise scale

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