Senior AIOps Engineer I

Location: Remote in the United States, excluding Alaska, Delaware, Hawaii, Mississippi, Nebraska, Montana, New Hampshire, West Virginia, and the District of Columbia; non-Seattle.

Salary: $135,000–$189,000 annually, plus potential long-term incentive awards.

Company

hirify.global is a vertical software and insurance platform providing home services software and homeowner protection products through relationships with approximately 30,000 companies involved in home buying.

What you will do

• Own production reliability for AI/ML services by defining SLOs and SLIs, monitoring availability, performance, model drift, and recurring retraining and redeployment cycles.
• Design monitoring, logging, alerting, dashboards, and deployment workflows for models and data pipelines, including blue/green, canary, A/B, and shadow deployments.
• Provision and operate AI infrastructure on Kubernetes and GCP, including AI gateways, RAG platforms, LLM observability tools, agentic workflows, and batch prediction services.
• Build automation for model promotion, configuration, environment management, Docker images, and multi-business-unit infrastructure provisioning.
• Operate Label Studio and its GCS, BigQuery, database, SSO, and annotation-cycle dependencies; build vector database, embedding, and context-retrieval infrastructure.
• Monitor LLM usage and costs, participate in on-call rotations, resolve incidents, lead post-incident reviews, and document architecture and operational standards.

Requirements

• 4+ years of applicable experience, including at least 3 years in commercial software engineering or production operations such as SRE, DevOps, Platform, or ML Platform.
• Experience operating production services at scale, preferably distributed, microservices-based, or Kubernetes-based systems.
• Experience operating Kubernetes and/or major cloud platforms; strong GCP experience is preferred, including GKE, BigQuery, Pub/Sub, Vertex AI, Cloud SQL, GCS, and IAM.
• Hands-on experience with CI/CD, deployment automation, infrastructure as code, monitoring, logging, alerting, containers, and the Docker image lifecycle.
• Experience installing, configuring, upgrading, securing, and maintaining self-hosted open-source tools on Kubernetes, including backup and disaster recovery.
• Understanding of machine learning concepts and model lifecycles, including training, evaluation, deployment, and monitoring.
• Strong computer science and programming fundamentals with proven ability to write high-quality software using TDD, SOLID principles, clean code, testing, code reviews, CI/CD, security, and documentation.
• Ability to communicate effectively with data scientists, ML engineers, and product stakeholders in business and technical contexts.
• Comfort participating in an on-call rotation and working collaboratively.

Nice to have

• Familiarity with AI gateways, vector databases, RAG pipelines, embedding models, or LLM evaluation frameworks.
• Experience with Python-based data and ML tools including NumPy, SciPy, Pandas, Scikit-Learn, and PyTorch.
• Experience building or operating ML infrastructure such as feature stores, orchestration platforms, model serving, and annotation tools.

Culture & Benefits

• Full-time remote work with eligibility limited to approved US locations.
• Medical, dental, vision, critical illness, hospital indemnity, accident, life, disability, HSA, FSA, and 401(k) benefits with discretionary employer match.
• Flexible paid vacation, typically nine company-paid holidays, paid sick time, and paid parental leave.
• Wellbeing, mental health, identity theft, travel assistance, fitness, and other discount programs.
• Inclusive culture focused on belonging, diverse perspectives, and collaborative work.

Hiring process

• Submit an application for review by the Talent Acquisition team.
• Selected candidates may have an introductory conversation followed by virtual interviews.