Remote Senior AIOps & ML Platform Engineer

Porch Group; Oregon; Hybrid; USD 135,000 - 189,000; Full time

Benefits offered by this job

Health insurance

401(k) plan

Paid time off

Holidays

Job summary

Porch Group is seeking a Senior AIOps Engineer I to partner with product managers, platform engineers, data scientists, and ML engineers. You will ensure AI/ML systems are reliable, observable, secure, and cost-efficient in production, focusing on monitoring model performance, drift, and deployment pipelines on Kubernetes (GKE) and Google Cloud Platform.

You will design and implement monitoring, CI/CD, and data pipelines, while provisioning new AI infrastructure and APIs.

Qualifications

• 4+ years of applicable experience in commercial software engineering or production operations (SRE/DevOps/Platform/ML Platform).
• Experience operating on Kubernetes and/or major cloud platforms.
• Hands-on experience with CI/CD tools and deployment automation.
• Strong understanding of observability, monitoring, and alerting for AI/ML systems.

Responsibilities

• Own production reliability for AI/ML services, defining SLOs/SLIs and model refresh cycles.
• Design monitoring, logging, and alerting for models and data pipelines with observability stacks.
• Streamline deployment workflows with data scientists and ML engineers, including blue/green and canary deployments.
• Provision, deploy, and maintain AI infrastructure (gateways, RAG platforms, LLM observability).
• Build automation to reduce manual operational work and support multi-BU infrastructure provisioning.
• Own the health of Label Studio production data annotation platform and related pipelines.
• Stand up RAG and vector database infrastructure and optimize embedding retrieval at scale.
• Monitor and optimize LLM token usage and costs, with centralized cost dashboards.

Skills

Kubernetes

Python

GCP

CI/CD

Terraform

Datadog

ML tooling

Tools

Docker

GitHub Actions

Job description

Porch Group is seeking a Senior AIOps Engineer I to partner with product managers, platform engineers, data scientists, and ML engineers. You will ensure AI/ML systems are reliable, observable, secure, and cost-efficient in production, focusing on monitoring model performance, drift, and deployment pipelines on Kubernetes (GKE) and Google Cloud Platform.

You will design and implement monitoring, CI/CD, and data pipelines, while provisioning new AI infrastructure and APIs.