DatabaseEngineering-LeadEngineer

## Accountabilities Architect, build, and operate production-grade AI/ML and database infrastructure supporting large-scale AI applications. Own the full lifecycle of database systems including OpenSearch, DocumentDB, Aurora PostgreSQL, and Redis across performance, scaling, and disaster recovery. Design and implement infrastructure as code using Terraform, Crossplane, and CloudFormation for cloud-native environments. Develop and maintain CI/CD pipelines for ML systems, including automated testing and model validation workflows. Implement monitoring, logging, and alerting systems using CloudWatch, Grafana, and related observability tools. Optimize vector search and embedding systems for retrieval-augmented generation (RAG) use cases. Support Kubernetes-based ML workloads including GPU scaling, service mesh, and performance tuning. Ensure database security through encryption, IAM policies, TLS configurations, and fine-grained access controls. Participate in rotating on-call support for production systems operating in a 24x7 environment. Requirements: 8+ years of experience in platform engineering, infrastructure, or database engineering roles. At least 3+ years of hands-on experience in AI/ML infrastructure or production ML systems. Strong experience with model serving frameworks such as SageMaker, Bedrock, vLLM, or TGI. Deep knowledge of vector databases and search systems, including OpenSearch k-NN indexing and embedding optimization. Strong Kubernetes (EKS) experience, including GPU workloads, autoscaling, and distributed system operations. Experience designing and operating cloud-native databases such as PostgreSQL, Redis, and document stores at scale. Strong understanding of LLM application patterns including retrieval systems, memory management, and agent frameworks (LangChain, LlamaIndex). Experience building infrastructure as code using Terraform, CloudFormation, or similar tools. Strong expertise in monitoring, observability, and incident response in product