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# AIOps Engineer

- **Company:** Porch
- **Location:** Seattle, WA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $135,000 - $189,000
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Build Automation, BigQuery, Cloud Computing, Code Review, Databases, Continuous Integration, Data Warehousing, DevOps, Data Flow Control, Github, Identity and Access Management, Python (Programming Language), Machine Learning, Nagios, NumPy, Open Source Technology, Operational Databases, Commercial Software, Prometheus, Azure Machine Learning, SciPy, SQL Databases, AI Infrastructure, Datadog, Data Logging, Feature Store, Google Cloud, Test-Driven Development (TDD), Pytorch, Retrieval-Augmented Generation, Delivery Pipeline, Large Language Models, Grafana, Agentic-AI, Pandas, Containerization, AI Platforms, Gitlab-ci, Scikit Learn, Solid Principles, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Low Latency, Deployment Automation, Machine Learning Operations, Evaluation of Large Language Models, Terraform, AIOps, Data Pipelines, Docker, Microservices
- **Published:** November 4, 2026
- **Apply:** https://startup.jobs/senior-aiops-engineer-porch-india-private-limited-10663495

## About the Role

* 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, or Kubernetes-based architectures.
 * Strong GCP experience preferred, including hands-on work with GKE, BigQuery (data warehousing, SQL, schema management), Pub/Sub, Vertex AI (batch prediction, model deployment), Cloud SQL, GCS, and IAM.
 * Hands-on experience with CI/CD tools and deployment automation (GitLab CI/CD, GitHub Actions, or similar), infrastructure-as-code tools (preferably Terraform), monitoring, logging, and alerting tools (Datadog, Prometheus, Grafana, ELK/EFK, or similar), and container management and the Docker image lifecycle.
 * Experience operating and administering self-hosted open-source tools on Kubernetes, including installation, configuration, upgrades, security hardening, and backup and disaster recovery.
 * Understanding of basic machine learning concepts and model lifecycles (training, evaluation, deployment, monitoring), and familiarity with common ML tooling.
 * Familiarity with LLM ecosystem tooling (AI gateways, vector databases, RAG pipelines, embedding models, or LLM evaluation frameworks) is a strong plus.
 * Strong computer science and programming fundamentals, with solid problem-solving skills and a proven ability to write high-quality software using Test-Driven Development (TDD), SOLID principles, clean code, and robust documentation.
 * Experience with Python-based data and ML stacks (NumPy, SciPy, Pandas, Scikit-Learn, PyTorch) is a plus. Experience building or operating ML infrastructure (feature stores, orchestration platforms, model serving, annotation tools) is highly desirable.
 * Ability to communicate effectively with data scientists, ML engineers, and product stakeholders in both business and technical contexts.
 * Comfort participating in an on-call rotation and working in a trustful, respectful, and collaborative environment.

## Description

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.
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