Infrastructure Engineer II

Bank of America•Plano, TX

2d•Onsite

About The Position

At Bank of America, the Infrastructure Engineer II designs tools and services within a technical domain to enable business strategies while following architectural governance, standards, and policies. The role creates infrastructure tools and integrates them as services, and facilitates deployment by developing templates, playbooks, and automation. It also seeks opportunities to improve the efficiency of implementing and maintaining tools and services, with an emphasis on innovation and continuous improvement. The position focuses on designing, building, and optimizing enterprise-scale Kubernetes AI platform infrastructure for model development, training, inference, and supporting services. The engineer partners with AI/ML engineers, data scientists, DevOps teams, and infrastructure teams to deliver scalable, secure, observable, resilient, compliant, and high-performing platforms in complex enterprise environments. Bank of America describes its workplace culture as inclusive and supportive of teammates’ physical, emotional, and financial wellness, with competitive and flexible benefits. The company is committed to an in-office culture that supports collaboration, engagement, and career development, with role-specific flexibility based on responsibilities and business needs.

Requirements

At least 5 years of experience in Kubernetes, container platform engineering, Linux systems, or AI/ML infrastructure in enterprise environments.
Strong expertise in Kubernetes architecture, cluster deployment, orchestration, and lifecycle management.
Hands-on experience with Docker, container runtimes, Operators, and Helm charts.
Strong Linux administration and troubleshooting skills across compute, networking, and storage layers.
Proficiency in Python, shell scripting, YAML, and JSON for automation and platform engineering.
Experience with observability and monitoring tools such as Prometheus, Grafana, and logging frameworks.
Knowledge of Kubernetes security practices, including RBAC, network policies, pod security, secrets management, and vulnerability awareness.
Experience supporting AI/ML platforms, model deployment pipelines, or GPU-enabled workloads in enterprise environments.
Familiarity with CI/CD, GitOps, infrastructure automation, and environment standardization practices.
Strong problem-solving and analytical thinking skills in complex, fast-paced environments.
Clear written and verbal communication skills, including the ability to explain technical concepts to non-technical stakeholders.
Collaborative mindset and experience working across AI/ML, DevOps, CTO, CIO, and infrastructure teams.
Strong documentation discipline and attention to detail.
Ability to balance strategic thinking with hands-on execution and delivery.
Self-starter mindset with ownership, accountability, and continuous improvement orientation.
Bachelor’s degree or equivalent work experience.

Nice To Haves

Kubernetes certification such as CKA or CKAD.
Understanding of Vulnerability Management, Non-Permitted Technology Remediations, and Enterprise Controls.
Prior experience building agents and agentic AI solutions in enterprise environments.
Knowledge of Copilot Studio for building apps.
Experience with AI/ML frameworks and model-serving platforms such as PyTorch, TensorFlow, Triton, or vLLM.
Familiarity with GPU platforms, the NVIDIA software stack, or high-performance AI infrastructure.
Experience with OpenShift, hybrid cloud, or enterprise platform modernization initiatives.

Responsibilities

Provide subject matter expertise and consulting on a range of technologies, and assist Technical Analysts and Infrastructure Engineers in ensuring solutions comply with enterprise system design and engineering standards.
Assist in translating business requirements into technical definitions, reference models, blueprints, and deployment playbooks that comply with architecture standards and policies.
Assist in evaluating reference models, blueprints, and playbooks to ensure they are fit for purpose.
Develop software solutions to address manual, repeatable work and inefficient processes.
Conduct on-site evaluations of third-party products being considered for adoption by the firm.
Promote an inclusive and healthy working environment and help resolve organizational impediments and blockers.
Contribute to creating or selecting functional and non-functional product evaluation requirements within and across domains.
Design and engineer Kubernetes-based AI platform infrastructure for model development, training, inference, and supporting services.
Build and manage containerized environments using Docker, Kubernetes, Operators, and Helm charts across development and non-development environments.
Optimize cluster performance, workload placement, scalability, resiliency, and resource utilization for AI/ML workloads.
Implement security and compliance controls, including RBAC, network policies, pod security, secrets management, and enterprise standards.
Establish observability through monitoring, logging, dashboards, and alerting.
Develop automation for deployment, configuration, patching, and operational workflows using Python, shell scripting, and infrastructure-as-code practices.
Collaborate with AI/ML engineers, data scientists, DevOps, and infrastructure teams to translate business and technical requirements into scalable platform solutions.
Create and maintain architecture documents, runbooks, standards, and operational procedures to support reliable platform delivery.