Software Engineering & Development, SrAssc

We are seeking a highly motivated  Agentic AI DevOps Engineer to help build, automate, and operate a large-scale enterprise AI platform that hosts shared AI services, Agentic AI capabilities, LLM services, RAG platforms, and AI-powered business solutions across AWS and Azure environments.   This role is ideal for an engineer with strong cloud, Kubernetes, DevOps, and automation expertise who is passionate about working with cutting-edge AI technologies while supporting mission-critical enterprise platforms.

AI Platform Engineering Build, deploy, and support enterprise AI platform services across AWS and Azure. Implement scalable, secure, and highly available cloud infrastructure. Support Agentic AI, Generative AI, RAG, and LLM-based solutions.   Kubernetes Platform Operations Deploy, manage, and troubleshoot multi-tenant Kubernetes clusters in AKS and EKS. Configure namespaces, RBAC, ingress controllers, service mesh, and networking components. Support autoscaling, workload scheduling, cluster upgrades, and platform resiliency. Monitor cluster health, performance, security, and capacity utilization.   Infrastructure Automation Provision cloud infrastructure using Terraform. Develop reusable Infrastructure-as-Code modules and CI/CD deployment pipelines. Automate environment provisioning, application deployment, and operational tasks. Utilize AI-assisted tools and automation agents to improve operational efficiency.   LLMOps & MLOps Implement CI/CD pipelines for AI and ML solutions. Support model deployment, versioning, monitoring, and lifecycle management. Enable RAG pipelines, vector databases, and LLM integrations. Configure AI observability and performance monitoring.   Monitoring & AIOps Configure dashboards, alerts, and observability solutions. Implement monitoring using industry-standard platforms. Develop intelligent operational automation and AIOps capabilities. Support automated root cause analysis and anomaly detection initiatives.   ________________________________________   Required Qualifications   Experience 3+ years  of experience in Cloud Engineering, DevOps, Infrastructure Engineering, or MLOps. 2+ years  of hands-on experience administering Kubernetes  in enterprise environments, including Docker, Helm, cluster monitoring, alert configuration, and container security. Expertise in cloud infrastructure provisioning using Terraform, Harness CI/CD pipelines  and shell scripting. AWS experience, including EKS, IAM, VPC Networking, Lambda, CloudWatch, ECR, and S3. Azure experience, including AKS, Azure Networking, Entra ID, Azure Monitor, and Key Vault. Experience supporting cloud-native applications and production workloads. Strong communication, troubleshooting, and automation skills.

Kubernetes Administration certification. Experience with Generative AI or Agentic AI platforms. Familiarity with LLMOps, MLOps, or AI platform operations. Experience with RAG architectures and vector databases. Exposure to Databricks, MLflow, Azure ML, AWS Bedrock, AWS SageMaker, Azure AI Foundry, or OpenAI services. Knowledge of infrastructure automation using AI-assisted tools. Cloud certifications in AWS, Azure, or Terraform. About State Street Across the globe, institutional investors rely on us to help them manage risk, respond to challenges, and drive performance and profitability. We keep our clients at the heart of everything we do, and smart, engaged employees are essential to our continued success. We are committed to fostering an environment where every employee feels valued and empowered to reach their full potential. As an essential partner in our shared success, you’ll benefit from inclusive development opportunities, flexible work-life support, paid volunteer days, and vibrant employee networks that keep you connected to what matters most. Join us in shaping the future. As an Equal Opportunity Employer, we consider all