Senior AIOps Engineer

PORCH 💚

. Own production reliability for AI/ML services by monitoring availability and performance .

Posted 11/2/2026full-timeRemote • Washington • United StatesSenior💰 $135,000 - $189,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in managing AI/ML services, including production reliability, monitoring, and deployment workflows. Proficient in CI/CD practices, Kubernetes operations, and infrastructure management, with a strong focus on optimizing performance and cost-efficiency.

Highest-signal resume keywords

Production Reliability ManagementKubernetes OperationsCI/CD Pipeline ImprovementMonitoring And Alerting ToolsAI Infrastructure Provisioning

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Hard Skills

Python ProgrammingCI/CD ToolsInfrastructure-As-CodeMonitoring ToolsContainer ManagementMachine Learning ConceptsDistributed SystemsCloud PlatformsData PipelinesModel Lifecycle Management

Soft Skills

Effective CommunicationIncident ResolutionCollaboration

Tools & Technologies

DatadogTerraformGitLab CI/CDGitHub ActionsGCPKubernetesDockerBigQueryPub/SubVertex AI

Industry Keywords

AI/ML ServicesProduction OperationsSREDevOpsPlatform Engineering

Tech Stack

Tools & technologies

BigQueryCloudDockerGoogle Cloud PlatformGrafanaKubernetesMicroservicesNumpyPandasPrometheusPythonPyTorchScikit-LearnSQLTerraform

About the role

Key responsibilities & impact

Own production reliability for AI/ML services by monitoring availability and performance
Define SLOs and SLIs tied to user experience and business impact
Own recurring model refresh cycles, including retraining, validation, and redeployment
Design and implement monitoring, logging, and alerting for models and data pipelines
Integrate model and system metrics with observability stacks and dashboards including Datadog and Opik
Streamline AI service deployment workflows, including blue/green, canary, A/B, and shadow deployments
Improve CI/CD pipelines and release processes for AI services
Support productionization of image-based ML models and batch prediction workflows
Provision, deploy, configure, and maintain AI infrastructure including AI gateways, RAG platforms, LLM observability tools, agentic workflows, and no-code agent builders
Build automation and tooling for model promotion, configuration, environment management, and Docker image maintenance
Support multi-business-unit infrastructure provisioning across environments, projects, roles, and CI/CD integrations
Own operational health of Label Studio, including GCS buckets, BigQuery pipelines, database, and SSO dependencies
Support bi-weekly annotation project cycles
Stand up and operate RAG and vector database infrastructure, embedding pipelines, and context retrieval APIs
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
Recommend cost-efficient model routing and provider selection strategies
Participate in an on-call rotation for AI/ML systems
Triage and resolve production incidents
Lead and contribute to post-incident reviews
Develop production-grade Python services and tooling on Kubernetes
Apply testing, code reviews, CI/CD, observability, and security practices
Document standards, architecture diagrams, and operational playbooks for other teams
Partner with product managers, platform engineers, data scientists, and machine learning engineers

Requirements

What you’ll need

4+ years of applicable experience, including at least 3 years in commercial software engineering or production operations (SRE, DevOps, Platform, or ML Platform)
Experience operating services on Kubernetes and/or major cloud platforms
Experience operating production systems at scale, preferably distributed, microservices-based, or Kubernetes-based architectures
Strong GCP experience preferred, including GKE, BigQuery, Pub/Sub, Vertex AI, Cloud SQL, GCS, and IAM
Hands-on experience with CI/CD tools and deployment automation such as GitLab CI/CD or GitHub Actions
Experience with infrastructure-as-code tools, preferably Terraform
Experience with monitoring, logging, and alerting tools such as Datadog, Prometheus, Grafana, or ELK/EFK
Experience with container management and Docker image lifecycle
Experience operating and administering self-hosted open-source tools on Kubernetes, including installation, configuration, upgrades, security hardening, backup, and disaster recovery
Understanding of machine learning concepts and model lifecycles
Familiarity with common ML tooling
Familiarity with LLM ecosystem tooling is a strong plus
Strong computer science and programming fundamentals
Ability to write high-quality software using TDD, SOLID principles, clean code, and robust documentation
Experience with Python-based data and ML stacks such as NumPy, SciPy, Pandas, and Scikit-Learn is a plus
Experience with PyTorch is a plus
Experience building or operating ML infrastructure is highly desirable
Ability to communicate effectively with data scientists, ML engineers, and product stakeholders
Comfort participating in an on-call rotation
Must be eligible to work in the United States; Porch Group does not consider remote applicants from Alaska, Delaware, Hawaii, Mississippi, Nebraska, Montana, New Hampshire, West Virginia, or the District of Columbia

Benefits

Comp & perks

Long-term incentive awards, subject to program guidelines and approvals
Comprehensive health, life, and financial wellbeing coverage
Three medical plan options
Two dental plan options
Vision plan
Voluntary Critical Illness, Hospital Indemnity, and Accident plans
Partially employer-funded Health Savings Account
Flexible Savings Accounts for healthcare, dependent care, and transportation
Company-paid Basic Life and AD&D insurance
Short- and Long-Term Disability benefits
Voluntary Life and AD&D plans
Traditional and Roth 401(k) plans with discretionary employer match
Employer-paid Supportlinc wellbeing program
Guided meditation and mindfulness exercises
Mental health coaching and clinical care
Confidential resources including will preparation
LifeBalance discounts on gym memberships, travel, appliances, movies, pet insurance, and more
Flexible paid vacation
Company-paid holidays, typically nine per year
Paid sick time
Paid parental leave
Identity theft program
Travel assistance
Fitness and other discount programs
Remote work arrangement