ZipRecruiter

Log In

1

Senior Aiops Engineer Jobs in California (NOW HIRING)

Diverse Lynx

Senior AIOps ML Engineer

Woodland Hills, CA · On-site

$110K - $151K/yr

Diverse Lynx is a company seeking a Senior AIOps ML Engineer. The role involves designing and developing machine learning models and data engineering solutions for AIOps, focusing on multi-domain ...

ClifyX

Senior AIOpsML Engineer

Woodland Hills, CA · On-site

$110K - $151K/yr

As a Senior AIOps ML Engineer, the successful candidate will own the platform's intelligence layer and will be responsible for architecting and operating the Lakehouse, engineering data marts, and ...

Lam Research

Senior AI Observability engineer

Fremont, CA · On-site

$114K - $157K/yr

The group you'll be a part of We are seeking a hands-on Senior AIOps Reliability Engineer to build the AI-native operations layer for our hybrid enterprise estate. You will design and ship LLM-based ...

next page

Showing results 1-20

All JobsAiops Engineer JobsSenior Aiops Engineer JobsSenior Aiops Engineer Jobs in California

Senior Aiops Engineer information

What is a Senior AIOps Engineer?

Senior AIOps Engineers are experienced IT professionals who leverage artificial intelligence and machine learning to automate and optimize IT operations. They design, implement, and maintain AIOps solutions that help organizations proactively manage and resolve IT issues, improve system reliability, and enhance performance. These engineers often lead teams, set best practices, and work closely with developers, IT staff, and business stakeholders to ensure technology systems run smoothly and efficiently.

What are the key skills and qualifications needed to thrive as a Senior AIOps Engineer?

To excel as a Senior AIOps Engineer, you need deep expertise in IT operations, machine learning, and data analytics, typically supported by a degree in computer science or a related field. Proficiency with AIOps platforms (like Moogsoft or Splunk), cloud services (AWS, Azure, or GCP), and automation tools, as well as relevant certifications, is highly valued. Strong problem-solving abilities, communication skills, and a proactive mindset distinguish top performers in this role. Mastering these competencies enables effective automation of IT operations, rapid incident response, and continual improvement of system reliability.

What are some common challenges faced by Senior AIOps Engineers when implementing automation in large-scale IT environments?

Senior AIOps Engineers often encounter challenges such as integrating new automation tools with legacy systems, ensuring data quality for accurate anomaly detection, and managing the complexity of diverse IT infrastructures. They also need to address organizational resistance to change and establish trust in automated recommendations. Collaborating closely with IT operations, development, and business teams is essential to align automation initiatives with organizational goals and ensure smooth adoption.

What does a Senior Aiops Engineer do?

A Senior Aiops Engineer designs, implements, and manages AI-driven automation and monitoring systems to optimize IT operations and ensure system reliability. They analyze large datasets, develop predictive models, and use tools like machine learning and cloud platforms to improve infrastructure performance and incident response. Strong scripting, automation skills, and knowledge of AI and DevOps practices are essential for this role.

What is the salary of a Senior Aiops Engineer?

The salary of a Senior Aiops Engineer typically ranges from $110,000 to $160,000 annually, depending on experience, location, and company size. Senior roles often require expertise in cloud platforms, automation tools, and monitoring systems, which can influence compensation levels.

What are the most commonly searched types of Aiops Engineer jobs in California?

The most popular types of Aiops Engineer jobs in California are:

What are popular job titles related to Senior Aiops Engineer jobs in California?

For Senior Aiops Engineer jobs in California, the most frequently searched job titles are:

What job categories do people searching Senior Aiops Engineer jobs in California look for?

The top searched job categories for Senior Aiops Engineer jobs in California are:

What cities in California are hiring for Senior Aiops Engineer jobs?

Cities in California with the most Senior Aiops Engineer job openings:

Senior Aiops Engineer jobs near you
Infographic showing various Senior Aiops Engineer job openings in California as of August 2026, with employment types broken down into 91% Full Time, 6% Part Time, and 3% Contract. Highlights an 86% Physical, 5% Hybrid, and 9% Remote job distribution.

Senior AIOps ML Engineer

Prophecy Technologies

Los Angeles, CA • On-site

$112K - $154K/yr

Full-time

Re-posted 7 days ago

Job description

Role Overview:
The Senior AIOps ML Engineer will be responsible for designing, building, and optimizing a robust Lakehouse architecture for petabyte-scale multi-domain observability data. This role involves developing and deploying advanced machine learning models for AIOps, including streaming anomaly detection, root-cause analysis, and incident forecasting. Key responsibilities also include managing the end-to-end MLOps lifecycle, ensuring data quality and performance, and integrating AIOps insights with incident management platforms. The engineer will also focus on security and compliance observability, collaborating with security teams, and contributing to organizational engineering standards and mentorship.
Key Responsibilities:
  • Lakehouse Architecture & Data Engineering: Design and evolve Lakehouse schema (Delta Lake / Apache Iceberg) for multi-domain observability data at petabyte scale. Build and maintain robust ingestion pipelines from OTel Collector through Kafka to the Lakehouse, ensuring exactly-once semantics and strict schema enforcement. Implement dbt transformation models to generate mart-ready, denormalized fact and dimension tables. Define and enforce data quality contracts and SLAs. Optimize query performance utilizing partitioning strategies, Z-ordering, bloom filters, and materialized views.
  • ML Model Development & AIOps: Design, train, and deploy machine learning models for streaming multivariate anomaly detection, root-cause analysis, and incident forecasting. Build low-latency streaming inference pipelines (Flink / Spark Streaming) for real-time anomaly scoring. Develop sophisticated log intelligence models (clustering, NLP classification, error deduplication). Implement unsupervised and semi-supervised methods for User Experience frustration detection and KPI correlation. Own the ML feature store, managing feature engineering, versioning, and backfill pipelines. Instrument model performance tracking, including drift detection, accuracy monitoring, and automated retraining triggers.
  • AIOps Platform & Productionization: Design and operate the end-to-end AIOps workflow, spanning signal ingestion, feature computation, model inference, alert routing, and auto-remediation hooks. Build high-performance model serving infrastructure supporting real-time REST/gRPC endpoints and async batch scoring with strict p99 latency SLOs. Integrate AIOps insights with incident management platforms (PagerDuty, Opsgenie) and internal runbooks. Define and publish metrics from the Business KPI mart to quantify business impact.
  • Security & Compliance Observability: Partner with the Security team to build the Security mart schema, including threat feed ingestion, UEBA baselines, and CVE correlation pipelines. Train anomalous-access and lateral-movement detection models. Ensure all data handling adheres strictly to data residency requirements, PII masking standards, and audit-log protocols.
  • Collaboration & Engineering Standards: Define telemetry schema contracts with the OTel Instrumentation team. Author ML platform RFCs and contribute actively to observability data model standards. Mentor junior ML and data engineers and conduct rigorous design reviews.

Required Skills:
  • AI Agents
  • Kafka and Streaming technologies (Flink / Spark)
  • Lakehouse architectures (Delta Lake / Apache Iceberg)
  • Machine Learning (Anomaly detection, time-series analysis)
  • Observability tools and concepts (OTel, APM, Logs)
  • MLOps practices (feature store management, drift detection, model retraining)
  • Strong proficiency in SQL and Python

Qualifications:
  • 10+ years of experience in a relevant field.

Apply