AI Ops Engineer || Hybrid in Johnston, RI & Phoenix, AZ & Dallas, TX & Charlotte , NC

Title: AI Ops Engineer
Experience Required: 12 Years

Location: Phoenix, AZ, Plano, TX & Remote

Employment Type: Contract W2 & C2C
Job Description:

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

• 10 years of IT experience with 4 years in SRE/DevOps/AI Ops.

• 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).