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Key Responsibilities and Required Skills for AIOps Solution Architect

💰 $ - $

AIOpsDevOpsSite Reliability EngineeringCloudMachine Learning

🎯 Role Definition

The AIOps Solution Architect is a senior technical leader responsible for architecting, designing, and delivering scalable AIOps and observability solutions across enterprise environments. This role blends expertise in monitoring, machine learning for operations, streaming data platforms, ITSM workflows, cloud infrastructure, and automation to reduce mean time to detect (MTTD) and mean time to resolve (MTTR). The architect partners with SRE, DevOps, platform, data engineering, security, and business teams to define use cases (anomaly detection, root cause analysis, event correlation, automated remediation), conduct vendor evaluations and proofs-of-concept, build production-grade pipelines for telemetry ingestion, and operationalize ML/AI models for continuous observability and incident intelligence.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Senior Site Reliability Engineer (SRE) with observability ownership
  • Senior DevOps / Platform Engineer with monitoring/automation experience
  • Data Scientist / ML Engineer focused on time-series and anomaly detection
  • IT Operations Manager or ITOM Architect

Advancement To:

  • Head of AIOps / Director of Observability
  • Director of Site Reliability Engineering / Platform Engineering
  • Principal Solutions Architect — Cloud/Observability
  • Chief Reliability Officer / VP of Engineering Operations

Lateral Moves:

  • Platform/Cloud Infrastructure Architect
  • Observability/Telemetry Architect
  • ML/Ops or Data Platform Architect
  • IT Service Management (ITSM) Solutions Architect

Core Responsibilities

Primary Functions

  • Architect and deliver end-to-end AIOps solutions that ingest, normalize, and analyze high-volume telemetry (metrics, logs, traces, events) from cloud, on-premise and hybrid environments to enable automated detection, correlation, and remediation capabilities.
  • Lead technical discovery, requirements gathering and stakeholder alignment for enterprise monitoring and AIOps programs, converting business SLAs and reliability objectives into measurable SLIs and SLOs.
  • Design and implement telemetry pipelines using streaming platforms (Kafka, Kinesis), time-series databases (Prometheus, InfluxDB), log indexes (Elasticsearch, Splunk), and tracing systems (Jaeger, Zipkin) for scalable observability.
  • Define data models and schemas for telemetry normalization (OpenTelemetry, OTLP) and ensure consistent tagging, context propagation and metadata enrichment across services and infrastructure.
  • Build and productionize anomaly detection and root-cause analysis models (statistical, ML, deep learning) for noisy operational data, continuously validating model performance and retraining pipelines.
  • Implement event correlation and deduplication logic to reduce alert noise and create actionable incidents using tools such as BigPanda, Moogsoft, Dynatrace, or custom correlation engines.
  • Integrate AIOps platforms with ITSM and incident response tooling (ServiceNow, Jira, PagerDuty) to enable automated incident creation, ticket enrichment, and remediation workflows.
  • Design automated runbook and remediation playbooks (chatops, serverless functions, Kubernetes operators) to remediate common incidents and facilitate progressive automation.
  • Lead vendor evaluations, proof-of-concepts and technology selection for observability, AIOps, and monitoring tools — balancing features, scale, cost, and integration complexity.
  • Define and enforce best practices for tagging, metric cardinality management, sampling, retention policies and cost optimization for observability at scale.
  • Work with security and compliance teams to ensure observability data handling, retention, and access controls meet governance and regulatory requirements.
  • Develop performance, capacity planning and cost analysis of telemetry ingestion and storage and propose technical optimizations or architectural changes.
  • Create end-to-end deployment patterns for cloud-native observability: Kubernetes/Prow/Helm/operators, IaC (Terraform, Pulumi), and CI/CD pipelines for dashboards, alerts, and model deployments.
  • Drive cross-functional proofs-of-concept and pilot programs that demonstrate measurable reductions in MTTR, alert fatigue, and manual toil by leveraging machine learning and automation.
  • Mentor and upskill engineering teams on observability standards, AIOps patterns, machine-learning-in-ops concepts, and incident playbooks to foster adoption and operational maturity.
  • Establish KPIs and dashboards that measure AIOps program success: alert noise reduction, automated remediation rate, MTTR improvement, platform health and model accuracy metrics.
  • Design observability onboarding processes and templates to accelerate new service instrumentation, including SDKs, exporters, and standardized logging/metrics frameworks.
  • Implement secure telemetry ingestion, with encryption, access controls and role-based access to minimize exposure of sensitive operational data.
  • Collaborate with data engineering and MLOps to build retraining pipelines, model versioning, monitoring of model drift, and rollback strategies for production AIOps models.
  • Create robust documentation, runbooks, architecture diagrams and operational playbooks for observability, alerting, and remediation to support 24/7 operations and on-call handoffs.
  • Partner with business and product stakeholders to prioritize AIOps use cases that deliver highest ROI (customer-impacting incidents, service availability, SLA adherence).
  • Lead continuous improvement initiatives: post-incident reviews, RCA automation, and feedback loops to refine detection rules and model performance.
  • Provide technical leadership in multi-cloud observability strategies (AWS, Azure, GCP), including native integrations (CloudWatch, Azure Monitor, Stackdriver) and cross-account/cross-project data aggregation.
  • Define migration strategies from legacy monitoring tools to modern AIOps platforms, including phased cutovers, dual-running, data backfills and validation tests.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

  • Observability & AIOps platforms: hands-on experience with Dynatrace, Datadog, Splunk, Elastic, New Relic, BigPanda, Moogsoft, or equivalent.
  • Telemetry standards and tooling: deep knowledge of OpenTelemetry, Prometheus, Jaeger, Fluentd/Fluent Bit, Logstash, Beats.
  • Cloud-native infrastructure: AWS, Azure, GCP — including managed monitoring services (CloudWatch, Azure Monitor, GCP Operations).
  • Container orchestration and Kubernetes: designing observability for clusters, operators, service meshes (Istio) and container metrics.
  • Data streaming & processing: Kafka, Kinesis, Spark, Flink for real-time telemetry ingestion and feature engineering.
  • Time-series and log storage: Elasticsearch, InfluxDB, TimescaleDB, Splunk — data modeling, scaling, retention strategies.
  • Machine learning for operations: anomaly detection, clustering, classification, trend detection, and model evaluation metrics (precision/recall, ROC, drift detection).
  • Automation & runbooks: experience with Infrastructure as Code (Terraform), configuration management (Ansible, Chef), serverless remediation (AWS Lambda) and chatops integrations.
  • ITSM & incident management: ServiceNow, Jira, PagerDuty integration patterns and workflow automation.
  • Security & compliance for telemetry: data encryption, RBAC, audit logging and PII considerations in observability data.
  • Scripting and programming: Python, Go, or Java for building/customizing collectors, enrichment pipelines and ML models.
  • Monitoring & alerting strategy: SLO/SLI design, alert thresholds, noise reduction strategies and capacity planning.
  • CI/CD and MLOps: experience in building model CI/CD, model registries, container image pipelines and observability-as-code.
  • SQL and time-series query languages for analysis and dashboarding.

Soft Skills

  • Strong stakeholder management and ability to translate business reliability goals into technical requirements.
  • Excellent communication: produce clear architecture docs, proposals, and executive-level summaries.
  • Leadership and mentorship: coach engineers across SRE, DevOps, and platform teams on best practices.
  • Analytical thinker with strong problem-solving and root-cause analysis skills.
  • Project management and delivery orientation: run PoCs, prioritize backlog, and deliver production outcomes.
  • Customer-focused mindset: balance technical debt, ROI and user experience in reliability investments.
  • Collaborative team player who can influence cross-functional groups without direct authority.
  • Adaptable to fast-changing environments and comfortable making technical trade-offs under ambiguity.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor’s degree in Computer Science, Software Engineering, Information Systems, Data Science, or related technical discipline.

Preferred Education:

  • Master’s degree in Computer Science, Data Science, Machine Learning, or MBA with strong technical experience.

Relevant Fields of Study:

  • Computer Science
  • Data Science / Machine Learning
  • Software Engineering
  • Information Systems
  • Cloud Computing / Distributed Systems

Experience Requirements

Typical Experience Range: 6–12+ years of professional experience in monitoring, SRE/DevOps, data engineering, or ML engineering roles.

Preferred: 8+ years with at least 3–5 years specifically delivering AIOps, observability, or operational ML projects in production, plus demonstrable experience with cloud-native architectures, telemetry pipelines, and ITSM integrations.

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