Senior Infrastructure Engineer - DevOps

• Applies technical knowledge and problem-solving methodologies to projects of moderate scope, with a focus on improving the data and systems running at scale, and ensures end to end monitoring of applications
• Resolves most nuances and determines appropriate escalation path
• Executes conventional approaches to build or break down technical problems
• Drives the daily activities supporting the standard capacity process applications
• Partners with application and infrastructure teams to identify potential capacity risks and govern remediation statuses
• Considers upstream/downstream data and systems or technical implications
• Be accountable for making significant decisions for a project consisting of multiple technologies and applications
• Adds to team culture of diversity, opportunity, inclusion, and respect

• Uses enterprise-authorized AI capabilities within the work environment to accelerate monitoring and capacity analysis and documentation, validating outputs and handling operational data according to sensitivity and security requirements

• Applies reuse-first, AI-assisted approaches to identify recurring capacity risks and improve remediation workflows, ensuring changes are validated and aligned to resiliency and security expectations

Required qualifications, capabilities, and skills

• Formal training or certification on infrastructure engineering concepts and 3+ years applied experience 

   

• Hands-on experience in DevOps, with a strong focus on Jenkins, Kubernetes, and SDLC pipelines.
• Proven experience in deployment architecture and cloud best practices.
• Working knowledge of Linux systems (preferably RHEL), including strong command-line skills for day-to-day engineering and troubleshooting.
• Experience with managing EOL components and driving process improvements.
• Proficient in Git branch management and infrastructure security.
• Knowledge of setting up and managing Kafka and Elasticsearch, including their resiliency.
• Experience with Helm for Kubernetes package management.
• Experience with monitoring and observability tools like Grafana, Dynatrace, Splunk, DataDog, etc.

• Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support infrastructure engineering workflows with strong validation habits and awareness of data sensitivity

• Ability to review and validate AI-assisted recommendations before implementation, escalating when uncertain and ensuring outcomes align to resiliency, security, and auditability expectations

• Experience in financial services or a related industry.
• Certifications in cloud platforms (e.g., AWS, Azure, Google Cloud).
• Strong communication and collaboration skills.