AI Ops – Senior Architect

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
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).