SRE ML Ops - Architect

Architect - (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