SRE ML Ops - Architect

Lead (MLOps & Infrastructure Reliability) – P4 We are looking for an intelligent, resourceful, and highly skilled Lead Site Reliability Engineer to join our AIDA Engineering group. In this role, you’ll focus on architecting and maintaining cloud- based MLOps pipelines to enable scalable, reliable, and production-grade AI/ML workflows, working closely with AI engineers, data engineers, and software teams. You will work closely with application development teams to build scalable, containerized infrastructure to support a modern SDLC process. This group plays a critical role in ensuring the stability, reliability, and availability of mission-critical production applications across Wiley. What we’re looking for ● Up to 5 years of experience in a Site Reliability Engineering (SRE), DevOps, or Production Engineering role, with a deep understanding of SRE principles and best practices. ● Design, implement, and maintain cloud-native platforms to support AI and data workloads, with a focus on AI and data platforms such as Databricks and AWS Bedrock. ● Hands-on experience with Databricks MLFlow, including model registration, versioning, asset bundles, and model serving workflows. ● Build and manage scalable data pipelines to ingest, transform, and serve data for ML and analytics. ● Proficiency in at least one coding language (Python, Go, Rust, or Typescript) for automation and debugging. ● Hands-on experience in Kubernetes (K8s) for managing and orchestrating containerized applications. ● Cloud experience (AWS preferred) with exposure to key services like EC2, S3, Lambda, and Bedrock– GCP a plus. ● Incident management expertise, including triaging, escalation, and resolution of high- severity outages. ● Strong troubleshooting and problem-solving skills, with experience diagnosing complex production issues. ● Excellent communication skills to articulate technical challenges and solutions effectively. Preferred skills Experience writing, maintaining and contributing to Terraform. Experience with AI-native Vector databases, ex. Weaviate. Architecting and managing AI/ML workloads in production using the employing of