Job Description:This job is responsible for tool and service designs within a technical domain that enable business strategies in accordance with architectural governance, standards and policies. Key responsibilities include creating infrastructure tools and their integration as a service, facilitating deployment of technical solutions by developing templates, playbooks and automation used during implementation. Job expectations include looking for opportunities to improve efficiency when implementing and maintaining tools/services and embracing a culture of innovation and continuous improvement.
We are seeking an experienced Kubernetes AI Engineer to design, build, and optimize enterprise-scale AI platform infrastructure that supports model development, training, and inference workloads. This role requires deep expertise in Kubernetes, containers, Linux, and platform engineering, with a strong focus on scalability, security, performance, observability, and automation. The ideal candidate will partner closely with AI/ML engineers, data scientists, DevOps teams, and infrastructure teams to deliver resilient, compliant, and high-performing AI platforms in complex enterprise environments.
Responsibilities:- Provides subject matter expertise and consulting services on a range of technologies and assists Technical Analysts and Infrastructure Engineers to ensure that technology solutions comply with enterprise system design and engineering standards
- Assists with translating business requirements into technical definitions, reference models, blueprints, and playbooks for deployment in compliance with architecture standards and policies
- Assists in the evaluation of reference models, blueprints and playbooks to ensure they are fit for purpose
- Develop software solutions to address manual and repeatable work or inefficient processes
- Conducts on-site evaluations of third-party products being considered for firm adoption
- Promotes an inclusive and healthy working environment and helps to resolve organizational impediments/blockers
- Contributes to the creation/selection of functional and non-functional product evaluation requirements within and across domains
Required Qualifications:- 5+ years of experience in Kubernetes, container platform engineering, Linux systems, or AI/ML infrastructure in enterprise environments.
- 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 using tools such as Prometheus and Grafana.
- 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.
- 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 with 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.
Desired Qualifications- Kubernetes certifications such as CKA or CKAD.
- Understanding of Vulnerability Management, Non-Permitted Technology Remediations, Enterprise Controls
- Prior experience building agents and agentic AI solutions in enterprise
- Knowledge of Copilot Studio to build apps would be nice to have
- Experience with AI/ML frameworks and model-serving platforms such as PyTorch, TensorFlow, Triton, or vLLM.
- Familiarity with GPU platforms, NVIDIA software stack, or high-performance AI infrastructure.
- Experience with OpenShift, hybrid cloud, or enterprise platform modernization initiatives.
Skills:- Analytical Thinking
- Application Development
- Automation
- Production Support
- Risk Management
- Adaptability
- Business Acumen
- DevOps Practices
- Solution Delivery Process
- Solution Design
- Architecture
- Collaboration
- Innovative Thinking
- Stakeholder Management
- Technical Strategy Development
Minimum Education Requirement: Bachelor's degree or equivalent work experience.
Shift:1st shift (United States of America)
Hours Per Week: 40
