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Job Title: AI Ops Senior Architect

Location - Phoenix, AZ (Hybrid)

Exp 15+ Years

We are seeking a highly skilled AI Ops Senior Architect to lead the design, implementation, and optimization of AI-driven operational platforms across large-scale, mission-critical environments. The ideal candidate will possess deep expertise in machine learning enabled operations, observability, automation frameworks, cloud engineering, and enterprise SRE/DevOps practices. This role will drive the transformation of traditional IT operations into intelligent, autonomous, self-healing systems.

The Senior Architect will collaborate with cross-functional engineering, cloud, platform, and data science teams to deliver predictive, proactive, and automated operational outcomes.

Key Responsibilities

• AI-Driven Operations Architecture
• Lead the architecture and implementation of AI-powered operational frameworks, including predictive analytics, anomaly detection, NLP-driven automation, and auto-remediation systems.
• Define and evolve the overall AI Ops strategy, roadmap, standards, and governance.
• Implement intelligent monitoring and decision models that enhance reliability and operational efficiency.
• Architect solutions that integrate machine learning models into production operations workflows.

Observability, Monitoring & Automation

• Design end-to-end observability ecosystems (metrics, logs, traces, topology, events) integrated with AI/ML platforms.
• Build anomaly detection models using ML and time-series analysis to identify issues before failures occur.
• Drive automated incident detection, impact assessment, and classification using AI-based models.
• Implement proactive auto-healing and automated resolution workflows.

Cloud & Platform Engineering

• Architect scalable AI Ops platforms using AWS, Azure, or Google Cloud Platform cloud-native services.
• Design infrastructure and pipelines for AI-driven monitoring and operational insights.
• Integrate AI Ops capabilities with Kubernetes, service mesh, cloud-native microservices, and distributed systems.
• Optimize cost, performance, and reliability using intelligent orchestration and scaling.

Data Engineering & ML Ops Integration

• Partner with data engineering teams to build robust data pipelines for operational data ingestion.
• Work with ML Ops teams to operationalize ML models, including training, evaluation, deployment, and monitoring.
• Ensure continuous retraining and drift detection for AI Ops models.
• Define data taxonomies, quality standards, and metadata management for operational datasets.

SRE, DevOps & Automation Frameworks

• Align AI Ops with SRE principles, SLIs, SLOs, and error budgets.
• Integrate AI-driven insights into CI/CD pipelines and operational workflows.
• Develop event-driven, automated runbooks using ML and rule-based systems.
• Implement intelligent capacity planning, scaling, and resource optimization.
• Security, Compliance & Governance
• Ensure AI Ops solutions meet enterprise security, compliance, and audit requirements.
• Define governance frameworks for AI model usage, transparency, and monitoring.
• Collaborate with cybersecurity teams on intelligent threat detection and risk analysis.

Leadership & Collaboration

• Provide architectural leadership and technical direction to engineering and operations teams.
• Mentor teams on AI Ops concepts, automation, and intelligent operations.
• Present architecture proposals and operational improvements to leadership stakeholders.
• Influence enterprise-wide transformation toward autonomous operations.

Required Skills & Experience

12+ years of IT experience with 5+ years in SRE/DevOps/AI Ops architecture.

Strong expertise in:

• AI Ops platforms (Moogsoft, Dynatrace Davis AI, BigPanda, New Relic AI, Datadog AIOps)
• Observability stacks (Prometheus, Grafana, ELK, Splunk, AppDynamics)
• ML pipelines and ML Ops tooling (SageMaker, Vertex AI, MLflow, Databricks)
• Cloud architectures on AWS / Azure / Google Cloud Platform
• Event-driven systems and automation tools
• Strong programming/scripting in Python, Go, or Java for automation and ML integration.
• Experience with Kubernetes, Docker, microservices, and distributed systems.
• Deep understanding of time-series analysis, anomaly detection, NLP, and predictive analytics.
• Experience operationalizing ML models and integrating them into production systems.

Preferred Qualifications

• Certifications in cloud architecture or ML engineering.
• Background in enterprise-scale SRE, observability, or operations automation.
• Experience with LLM-based automation and AI agents for IT operations.
• Experience in highly regulated industries (Finance, Healthcare, Telecom).