• Design, implement, and maintain cloud-native platform to support AI and data workloads, with a focus on AI and data platforms such as Databricks and AWS Bedrock. • Build and manage scalable data pipelines to ingest, transform, and serve data for ML and analytics. • Develop infrastructure-as-code using tools like Cloudformation, AWS CDK to ensure repeatable and secure deployments. • Collaborate with AI engineers, data engineers, and platform teams to improve the performance, reliability, and cost-efficiency of AI models in production. • Drive best practices for observability, including monitoring, alerting, and logging for AI platforms. • Contribute to the design and evolution of our AI platform to support new ML frameworks, workflows, and data types. • Stay current with new tools and technologies to recommend improvements to architecture and operations. • Integrate AI models and large language models (LLMs) into production systems to enable use cases using architectures like retrieval-augmented generation (RAG).

• Design, deploy, and operate Kubernetes clusters (EKS or self-managed) on AWS, ensuring high availability and security • Build and maintain CI/CD pipelines and internal developer tooling to improve engineering velocity • Automate infrastructure provisioning and operational tasks using Python and tools like Terraform, OpenTofu, and Ansible • Define and enforce platform standards around observability, cost management, and incident response • Partner with application teams to support containerized workloads and resolve infrastructure bottlenecks • Collaborate with Customer Success teams by providing reliable and scalable tooling that supports seamless customer onboarding, integrations, and service delivery

• Ensure Midnite's reliability, performance, and availability. • Shape the evolution of the core platform and set engineering standards. • Drive improvements across infrastructure and backend systems. • Work closely with the Head Of Platform Engineering and the global Platform team. • Manage a small team of engineers, providing direction and coaching. • Regularly contribute code, reviews, and production changes.

• Configure and wire Azure services within pre-provisioned environments to support application workloads • Implement data ingestion pipelines using Azure services • Configure and manage: o Blob Storage o Event-driven architecture o Cloud-based databases • Set up secure access using managed identities and role-based access control • Support application deployment and CI/CD workflows • Write lightweight Python scripts for automation and integration • Run AzCopy operations for large-scale data backfills from source systems into Blob Storage • Configure Application Insights for monitoring, logging, and alerts • Create clear documentation and operational runbooks • Collaborate with application engineers to ensure seamless platform integration