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Large financial brand and a common DevOps role increase competition despite seniority.
Requires specialized AI/ML platform and enterprise finance experience so cross-industry transferability is moderate.
Explicit 12+ years requirement plus mandatory cloud, Kubernetes, Terraform, and MLOps skills make filtering strict.
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Design, build, and maintain scalable, secure cloud infrastructure and CI/CD pipelines supporting AI/ML workloads.
Deploy and manage containerized applications using Kubernetes and automate infrastructure with IaC tools like Terraform and Ansible.
Implement MLOps workflows including model deployment, monitoring, and optimize cloud resources for AI workloads' performance and cost.
8–12+ years of professional experience in DevOps or software engineering roles supporting AI/ML platforms.
Degree requirement: B.S. in Computer Science, Engineering, Mathematics, Physics, or equivalent; MS preferred.
Mandatory skills: Experience with cloud platforms (AWS, Azure, GCP), Kubernetes, Docker, Terraform/Ansible, CI/CD tools (Jenkins/GitHub Actions/GitLab CI), Linux administration, plus MLOps tools (MLflow, Kubeflow).
Experience required in enterprise software development including Java, C#, SQL, with familiarity in microservices, Spring Boot, message systems (Kafka), and agile methodologies.
Experienced in delivering large-scale, complex cloud-native AI/ML platforms with strong operational ownership and troubleshooting capabilities.
Skilled in collaborating with cross-functional teams (data scientists, engineers) to create sustainable, secure platform solutions in financial services or similar regulated industry.
Capability to lead architecture design and implement automation for infrastructure provisioning and deployment aligned with DevSecOps and observability best practices.