Senior Cloud Engineer - GenAI Platform Engineering

Responsibilities

• Ensures that the design and engineering approach for complex features are consistent with the larger portfolio solution
• Defines the technology tool stack for the solution and evaluates and adapts new testing tool/framework/practices for team(s)
• Enables team(s)/applications with Continuous Integration/Continuous Development (CI/CD) capabilities and engages with other technical stakeholders pertaining to efficient functioning of CI-CD pipeline
• Guides and influences team(s) on design and best practices for high code performance – e.g. pairing, code reviews
• Provides end-to-end delivery of complex features, including automation, for either a single team or multiple teams, at the program level
• Conducts research, design prototyping and other exploration activities such as evaluating new toolsets and components for release management, CI/CD, and features
• Works with stakeholders to establish high-level solution needs and with architects for technical requirements
• Designs, implements, and manages containerized environments (Kubernetes and managed services such as EKS, AKS, GKE)
• Develops and maintains scalable deployment and release pipelines
• Engineers highly scalable cloud architectures

Requirements

• 7+ years in cloud engineering, platform engineering, or DevOps/SRE roles
• Strong hands-on experience with: AWS, Azure, and/or GCP (multi-cloud experience preferred)
• Container platforms (Kubernetes, EKS, AKS, GKE)
• Infrastructure-as-code and CI/CD pipelines
• Proven experience designing and operating: highly scalable distributed systems, containerized workloads in production cloud environments
• Expertise in: scaling strategies (autoscaling, load balancing, horizontal scaling), performance tuning and system optimization, cloud-native architectures and microservices
• Experience with: observability tools (metrics, logging, distributed tracing), reliability engineering (high availability, fault tolerance, disaster recovery)

Qualifications

• Experience supporting AI/ML or GenAI workloads, including: model inference endpoints and API gateways, high-throughput, low-latency serving systems
• Familiarity with: vector databases, data pipelines, or retrieval-based architectures, scaling patterns for compute-intensive workloads
• Experience in: regulated enterprise environments (financial services preferred), shared, multi-tenant platform environments supporting multiple teams

Skills

• Automation
• Influence
• Result Orientation
• Stakeholder Management
• Technical Strategy Development
• Application Development
• Architecture
• Business Acumen
• Risk Management
• Solution Design
• Agile Practices
• Analytical Thinking
• Collaboration
• Data Management
• Solution Delivery Process