Senior AIOps Engineer

Porch.com; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Porch Group in the United States is seeking a Senior AIOps Engineer I to ensure AI/ML systems are reliable, observable, and cost-efficient in production. You will partner with product managers, platform engineers, data scientists, and ML engineers to ship AI features safely on Kubernetes (GKE) and Google Cloud Platform.

You'll own monitoring, deployment pipelines, incident response, and build infrastructure with tools like Union Cloud, BentoML, Feast, and BigQuery, while contributing to cost

Qualifications

• 4+ years in software engineering or production operations.
• Experience with Kubernetes and major cloud platforms.
• Focus on reliability, observability, and cost efficiency in AI/ML services.

Responsibilities

• Own production reliability for AI/ML services with SLOs/SLIs.
• Design monitoring, logging, and alerting for models and data pipelines.
• Improve CI/CD and deployment processes for AI services.
• Deploy and manage AI infrastructure including LLM gateways and RAG platforms.
• Lead on-call rotations and post-incident reviews.
• Develop production-grade Python services on Kubernetes with best practices.

Skills

Python

Kubernetes

CI/CD

Observability

SRE/DevOps

Cost optimization

Data analysis

Tools

Union Cloud

BentoML

Feast

Datadog

BigQuery

GCP

Terraform

Job description

Porch Group is a leading vertical software and insurance platform and is positioned to be the best partner to help homebuyers move, maintain, and fully protect their homes. We offer differentiated products and services, with homeowners insurance at the center of this relationship. We differentiate and look to win in the massive and growing homeowners insurance opportunity by 1) providing the best services for homebuyers, 2) led by advantaged underwriting in insurance, 3) to protect the whole home.

As a leader in the home services software-as-a-service ("SaaS") space, we've built deep relationships with approximately 30 thousand companies that are key to the home-buying transaction, such as home inspectors, mortgage companies, and title companies.

In 2020, Porch Group rang the Nasdaq bell and began trading under the ticker symbol PRCH. We are looking to build a truly great company and are JUST GETTING STARTED.

Job Title: Senior AIOps Engineer I

Location: United States

Workplace Type: Remote

Job Summary

The future is bright for the Porch Group, and we'd love for you to be a part of it as our Senior AIOps Engineer I.

In this role, you will partner with product managers, platform engineers, data scientists, and machine learning engineers to make sure our AI and ML-powered systems are reliable, observable, secure, and cost-efficient in production. You will focus on how AI systems run in the real world: monitoring model performance and drift, keeping deployment pipelines robust, managing incidents, and standing up new AI infrastructure. Our AI/ML stack is built on Python and runs on Kubernetes (GKE) and Google Cloud Platform, using tools such as Union Cloud (Flyte), BentoML, Feast, Label Studio, BigQuery, and Dataflow. On the GenAI side, we operate a centralized LLM routing and gateway service, run batch prediction services for large-scale inference, and are building out RAG infrastructure. You will help evolve this stack so teams can ship AI features quickly and safely.

What You Will Do As A Senior AIOps Engineer I

• Own production reliability for AI/ML services by monitoring availability and performance, defining SLOs and SLIs (latency, error rates, model performance) tied to user experience and business impact, and owning recurring model refresh cycles (retraining, validation, and redeployment) to prevent staleness and drift.
• Design and implement monitoring, logging, and alerting for models and data pipelines, and integrate model and system metrics with existing observability stacks and dashboards (Datadog, Opik, and others).
• Streamline deployment workflows with data scientists and ML engineers (blue/green, canary, A/B, and shadow deployments), improve CI/CD pipelines and release processes for AI services, and support the productionization of image-based ML models, including batch prediction workflows.
• Provision, deploy, configure, and maintain new AI infrastructure as it is adopted, including AI gateways, RAG platforms, LLM observability tools, agentic workflows, and no-code agent builders, using tools such as Union Cloud, BentoML, Feast, Kubernetes, Terraform, and GCP services.
• Build automation and tooling to reduce manual operational work around model promotion, configuration, environment management, and Docker image maintenance, and support multi-BU infrastructure provisioning (separate environments, projects, roles, and CI/CD integrations).
• Own the operational health of Label Studio, our production data annotation platform, including supporting GCS buckets, BigQuery pipelines, and database and SSO dependencies, and supporting bi-weekly annotation project cycles.
• Stand up and operate RAG and vector database infrastructure, including embedding pipelines and context retrieval APIs, and optimize embedding storage and retrieval performance at scale.
• Monitor and optimize LLM token usage and costs across providers, maintain centralized cost tracking dashboards and alerting across business units, and recommend cost-efficient model routing and provider selection strategies.
• Participate in an on-call rotation for AI/ML systems, triage and resolve production incidents, and lead and contribute to post-incident reviews that drive lasting improvements.
• Develop production-grade services and tooling, primarily in Python, on Kubernetes, applying strong engineering practices (testing, code reviews, CI/CD, observability, security), and document standards, architecture diagrams, and operational playbooks for other teams.

What You Will Bring As A Senior AIOps Engineer I

• A curious and driven mindset, with a strong interest in how AI systems behave in production and how to make them more reliable and observable.
• 4+ years of applicable experience, including at least 3 years in commercial software engineering or production operations (SRE, DevOps, Platform, or ML Platform), operating services on Kubernetes and/or major cloud platforms.
• Experience operating production systems at scale, preferably distributed, microservices-based