Ananya Kurukundu

AI Platform & MLOps Engineer with 7+ years of experience designing, building, and operating enterprise-scale AI/ML platforms, cloud-native infrastructure, and production environments across AWS, Azure, and Google Cloud Platform (GCP). Specialized in AI Platform Engineering, MLOps, Generative AI, Kubernetes, Infrastructure as Code (IaC), and platform automation. Proven expertise delivering secure, scalable, and production-grade environments that enable data scientists, ML engineers, and application teams to develop, train, deploy, monitor, and operationalize machine learning and GenAI solutions at scale.

AI Platform Engineering & MLOps: Built and operated end-to-end AI platforms supporting model development, training, deployment, inference, monitoring, and lifecycle management using Amazon SageMaker, Amazon Bedrock, Kubernetes, and cloud-native services. Developed reusable platform capabilities that accelerated AI adoption, standardized deployments, and improved operational efficiency across enterprise environments. Implemented Bedrock Knowledge Bases, OpenSearch Vector Search, and Retrieval-Augmented Generation (RAG) architectures to support scalable GenAI applications and enterprise knowledge discovery.

Cloud & Platform Architecture: Designed multi-account, multi-environment cloud platforms across AWS, Azure, and GCP, provisioning infrastructure through Terraform, Terragrunt, CloudFormation, Bicep, and Pulumi. Built reusable platform services including networking, compute, storage, data services, security controls, AI services, and Kubernetes platforms supporting enterprise, analytics, and AI/ML workloads.

Developer Platforms & Automation: Created self-service infrastructure and deployment capabilities using Infrastructure as Code, CI/CD automation, GitOps, and platform engineering best practices. Engineered automated software delivery pipelines using Azure DevOps, Jenkins, GitHub Actions, Spinnaker, CloudBees, Argo CD, and Flux, enabling rapid, consistent, and secure application and ML workload deployments.

Kubernetes & AI Infrastructure: Extensive experience deploying and managing AI, machine learning, and cloud-native workloads on Kubernetes platforms including EKS, AKS, and GKE. Skilled in Docker, Helm, Rancher, RBAC, ingress controllers, service mesh technologies, container orchestration, workload scaling, and platform reliability engineering. Supported migration of VMware and PKS workloads to Kubernetes-based cloud platforms and AI-ready environments.

GenAI, RAG & Intelligent Applications: Built infrastructure supporting Generative AI solutions using Amazon Bedrock, Knowledge Bases, OpenSearch, vector search technologies, semantic search pipelines, document ingestion frameworks, event-driven architectures, and Retrieval-Augmented Generation (RAG) frameworks. Enabled enterprise AI capabilities through secure model integration, platform governance, observability, and operational controls.

Data, Observability & Platform Reliability: Implemented comprehensive observability strategies using CloudWatch, Cloud Monitoring, Azure Monitor, Datadog, Dynatrace, Prometheus, Grafana, OpenTelemetry, Splunk, and ELK. Built monitoring, logging, tracing, alerting, and reliability frameworks for AI platforms, distributed applications, and Kubernetes environments while improving availability, performance, and operational excellence.

Security & Governance: Applied platform security and governance best practices using IAM, KMS, Secrets Manager, Azure Key Vault, Vault, OPA/Conftest, Snyk, Trivy, Qualys, network segmentation, RBAC, encryption, and least-privilege access controls to secure enterprise AI/ML platforms and cloud-native applications.

Platform Engineering & Cloud Transformation: Led initiatives involving cloud migration, platform modernization, infrastructure standardization, AI platform adoption, and enterprise application transformation. Collaborated with development, security, data engineering, and operations teams to deliver resilient, scalable, and production-ready AI-enabled cloud solutions. Supported AI research infrastructure on GCP by provisioning GKE clusters, Compute Engine instances, Cloud Storage, Cloud SQL, Jenkins CI/CD pipelines, and Kubernetes-based application deployments using Terraform and automation frameworks