Build, deploy, and troubleshoot microservices in Kubernetes and Amazon EKS, ensuring scalability and reliability.
Design secure, highly available web applications with a focus on capacity planning and performance optimization.
Deploy and manage the lifecycle of LLMs and embedding models, defining KPIs to measure and improve AI application performance.
Evaluate and integrate emerging technologies such as RAG systems, MCP servers, AI Agents, and agentic workflows into our platform.
Manage AWS core and GenAI services (S3, IAM, EKS, Bedrock, etc.) using infrastructure-as-code tools like Terraform and Chef, while maintaining observability through tools like New Relic or PagerDuty.
Collaborate across product, platform, and engineering teams on architecture design, security patching, incident response, and release management to ensure the reliability of our ML and GenAI infrastructure

Bachelor’s degree and 8+ years of experience managing large-scale cloud applications with a strong background in Linux administration and troubleshooting. Excellent communication skills, a collaborative mindset, and the confidence to take ownership, drive solutions, and deliver results independently while thinking globally.
Over 5 years of hands-on experience managing cloud infrastructure across AWS, GCP, and Azure environments.
A solid understanding of today’s generative AI ecosystem, with practical experience using LLMs and embedding models (OpenAI, AWS Bedrock, SageMaker); familiarity with vector databases like LanceDB is a plus.
Strong scripting skills in Bash or Python, and experience with container orchestration platforms like Amazon EKS or Azure AKS.
Proficiency with DevOps and automation tools such as Chef, GitHub Actions, Rundeck, and IaC frameworks like Terraform, Spacelift, and Helm.
Working knowledge of DNS, load balancers, and MySQL, along with a good grasp of source control and branching strategies in Git.

Lead Site Reliability Engineer

Key skills