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Senior Cloud Engineer – GenAI Platform Engineering
Bank of AmericaCore Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in cloud engineering and platform engineering, with a strong focus on designing and operating scalable cloud architectures, containerized environments, and CI/CD pipelines. Proven ability to lead technical initiatives and collaborate across teams to deliver complex features and solutions in regulated environments.
Highest-signal resume keywords
Cloud EngineeringKubernetesCI/CD PipelinesAWS, Azure, GCPDistributed Systems
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- Define and lead the engineering approach for complex features and significant business outcomes
- Deliver complex features and technology while enabling development efficiencies
- Provide technical thought leadership based on multiple software implementations
- Own end-to-end solution design and delivery
- Ensure design and engineering approaches align with the larger portfolio solution
- Define the technology tool stack and evaluate new testing tools, frameworks, and practices
- Enable teams and applications with CI/CD capabilities
- Guide teams on design, coding best practices, and code performance
- Deliver complex features, including automation, for one or multiple teams at program level
- Conduct research, design prototyping, and exploration of new toolsets and components
- Establish high-level solution needs with stakeholders and technical requirements with architects
- Build and operate cloud infrastructure for the GenAI platform across AWS, Azure, and GCP
- Enable secure and scalable deployment of agent-based applications, APIs, and model-serving workloads
- Design, implement, and manage containerized environments using Kubernetes and managed services such as EKS, AKS, and GKE
- Develop and maintain scalable deployment and release pipelines
- Engineer highly scalable cloud architectures
- Collaborate with Global Markets Sales & Trading, QSDG, and Platform teams to design and build the global generative AI platform
Requirements
What you’ll need- 7+ years in cloud engineering, platform engineering, or DevOps/SRE roles
- Strong hands-on experience with AWS, Azure, and/or GCP
- Experience with Kubernetes, EKS, AKS, and GKE
- Experience with infrastructure-as-code and CI/CD pipelines
- Proven experience designing and operating highly scalable distributed systems
- Proven experience operating containerized workloads in production cloud environments
- Expertise in autoscaling, load balancing, horizontal scaling, performance tuning, and system optimization
- Expertise in cloud-native architectures and microservices
- Experience with observability tools, including metrics, logging, and distributed tracing
- Experience with reliability engineering, including high availability, fault tolerance, and disaster recovery
- Experience supporting AI/ML or GenAI workloads, including model inference endpoints and API gateways
- Familiarity with vector databases, data pipelines, or retrieval-based architectures
- Experience with scaling patterns for compute-intensive workloads
- Experience in regulated enterprise environments; financial services preferred
- Experience in shared, multi-tenant platform environments supporting multiple teams
- Bachelor Degree or Equivalent Professional Experience
- Ability to work 1st shift in the United States of America
- 40 hours per week
Benefits
Comp & perks- In-office culture with an appropriate level of flexibility based on role-specific responsibilities and business needs
- Opportunities to learn, grow, and make an impact
- Support for physical, emotional, and financial wellness
- Affordable, competitive and flexible benefits
- Annual discretionary plan eligibility
- Benefits eligibility
- Paid time off
- Resources and support for employees
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Cloud-Native ArchitecturesInfrastructure-as-CodePerformance TuningAutoscalingLoad BalancingReliability EngineeringObservability ToolsModel Inference EndpointsAPI GatewaysContainerized Workloads
Tools & Technologies
KubernetesEKSAKSGKEGenAI Platform
Industry Keywords
Financial ServicesRegulated EnvironmentsMulti-Tenant PlatformsAI/ML WorkloadsCloud Infrastructure