Senior II Software Engineer Lead - Akamai Inference Cloud

The Akamai Inference Cloud team is part of Akamai's Cloud Technology Group. They build AI platforms for efficient, compliant, and high-performing applications that support customers in running inference models and empower developers to create advanced AI solutions effectively.

• Possess software engineering expertise with advanced proficiency in distributed systems, cloud services, and platform engineering.
• Demonstrate deep experience building and operating internet-facing platform services such as API gateways, ingress layers, reverse proxies, or service infrastructure.
• Demonstrate advanced expertise in cloud-native architectures, incorporating containerization and orchestration technologies such as Kubernetes.
• Have proven experience building scalable, high-performance systems with modern DevOps practices, CI/CD pipelines, and infrastructure-as-code.
• Exhibit technical leadership by guiding, promoting code quality, and delivering solutions for intricate platform challenges.
• Have experience designing and operating API platforms, including authentication, authorization, traffic management, and service protection patterns.
• Demonstrate extensive expertise in observability, monitoring, alerting, and debugging processes for large-scale distributed systems.
• Have experience with capacity planning, performance analysis, and reliability engineering for production services.

• Lead the design and implementation of critical platform components for Akamai Inference Cloud, ensuring performance, scalability, and reliability.
• Drive technical decisions for your domain, selecting appropriate tools, frameworks, and architectural approaches for secure, scalable, and high-performance inference platform services.
• Lead the evolution of platform capabilities including authentication, load balancing, rate limiting, firewall integration, and externally facing API patterns.
• Design and improve data plane APIs, including OpenAI-compatible interfaces and other standards required to support diverse inference and AI application workloads.
• Define and advance platform observability through metrics, logging, tracing, monitoring, and operational diagnostics for distributed systems at scale.
• Drive capacity planning and scalability strategies to ensure the platform can support growth in traffic, models, and regional deployments.
• Lead Kubernetes-based orchestration patterns for platform services, helping ensure efficient, resilient, and operable deployment of containerized workloads.
• Mentor and guide engineers on the team through code reviews, design discussions, and technical problem-solving.