AI Platform Engineer

AI Platform Engineer We're looking for a handson AI Platform Engineer to own the infrastructure, operations, and SDLC for the Enterprise AI team. This role is focused on running, hardening, and evolving GenAI platforms , while also helping build internal platform tooling that enables GenAI development at SEI. You will support teams building GenAI solutions by providing secure, reliable, welloperated infrastructure , strong deployment standards, and clear operational guardrails. Success in this role is measured by system stability, security, scalability, and developer velocity across GenAI workloads. This is a role for an engineer with an operator mindset, strong cloud fundamentals, and deep ownership of production systems. What you'll do GenAI Platform Operations • Contribute to the runtime and operational posture for the Enterprise AI team, including RAG systems, inference services, evaluation jobs, and supporting pipelines. • Operate Azure Foundry/ Azure AI services, Azure AI Search (vector and hybrid), and related data access patterns. • Help define and maintain standardized platform patterns for GenAI infrastructure on the Enterprise AI team. SDLC Enablement • Enable secure and repeatable SDLC workflows for GenAI systems, including CI/CD, environment promotion, rollback strategies, and operational standards. • Work within the team to establish and evolve shared standards for building and operating GenAI systems. Infrastructure, Networking, and Security • Design, provision, and operate Azure infrastructure using Terraform . • Manage modular Terraform configurations across environments (dev, test, prod) with strong state and lifecycle discipline. • Interact with Azure networking, including VNets, subnet segmentation, private endpoints, firewall integration, routing, DNS, and egress control. • Ensure secure access to Azure resources using managed identities and RBAC, avoiding keybased access where possible. • Partner with security teams to meet enterprise and regulatory requirements. Reliability, Observability, and Operations • Implement observability by default using App Insights, Log Analytics, and OpenTelemetry. • Define and monitor SLIs and SLOs such as latency, availability, error rates, and cost signals. • Own incident response, root cause analysis, and operational runbooks for GenAI infrastructure. GenAI Controls and Governance • Operationalize quality and safety controls including evaluation automation, grounding checks, drift detection, and version management. • Support humanintheloop workflows and controlled rollout of model, prompt, and configuration changes. • Maintain operational documentation and auditready system visibility. What makes you a great fit Aptitude and attitude • You think like a platform engineer and anticipate failure modes before they reach production. • You prefer automation, repeatability, and clear abstractions over oneoff solutions. • You take ownership of systems end to end and communicate clearly with both engineers and stakeholders. Gen AI platform mindset • You understand how LLMs, embeddings, and RAG systems work, with a focus on operating them reliably . • You are comfortable enabling fastmoving GenAI development while enforcing necessary guardrails. • You want to help automate day to day operational tasks using AI. Qualifications Required • 5+ years of production software, platform, or cloud engineering experience using Python. • Terraform experience is required , including realworld ownership of infrastructure provisioning and change management. • Handson experience operating Kubernetes, API Gateways, VMSS(or similar autoscaling compute service), App Services (or similar PaaS service) • Knowledge of Azure networking, including VNets, private endpoints, firewall integration, routing, and DNS. • Strong DevOps foundations including Docker, Kubernetes or AKS, and CI/CD pipelines. • Experience implementing observability and monitoring for production systems. Preferred • Experience operating systems in regulated or enterprise environments. • Familiarity with feature flags, canary deployments, and progressive delivery. • Solid understanding of LLM operational concerns such as quotas, rate limits, latency, cost management, and failure modes. • Experience securing cloud systems using managed identities, RBAC, SSO Integration, and network isolation.