SRE - Architect

Site Reliability Engineer, 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