Job Title: Dynatrace Davis AI Observability Architect (AWS)
Location: Chicago, IL (Hybrid/Onsite Preferred)
Duration: Long-Term Contract
Experience: 10+ Years (Minimum 5+ Years in Dynatrace Architecture, 2+ Years applied Davis AI )
Employment Type: Contract / Full-Time
Job Summary
We are seeking a Dynatrace Observability Architect whose core differentiator is deep, hands-on mastery of Davis AI
(causal AI, anomaly detection, predictive analytics) and the emerging Dynatrace MCP Server ecosystem that
connects Dynatrace''s live observability data to AI coding assistants and agentic workflows (Claude, GitHub Copilot,
Cursor, Amazon Q, and similar MCP clients). This is an architect who can move Dynatrace beyond dashboards and
into autonomous, AI-driven operations.
This is not a support or administration role. We need someone who can design Davis AI-driven root cause
automation and stand up MCP-based agentic workflows that give AI assistants safe, governed, real-time access to
production telemetry.
Davis AI — Core Responsibilities
• Architect and tune Davis AI causal analysis across infrastructure, application, and business layers — not
just consuming default anomaly alerts, but customizing baselines, thresholds, and event correlation rules.
• Design and implement Davis AI-driven root cause automation, reducing MTTR by connecting Davis
detected problems directly to remediation workflows.
• Configure Davis anomaly detection for custom metrics, business events, and multi-dimensional analysis
(not just infrastructure defaults).
• Build and maintain Grail-based data models that feed Davis AI with high-quality, contextualized signals
(proper tagging, management zones, Smartscape topology) — data quality directly determines Davis AI
accuracy.
• Implement Davis CoPilot (GA) and evaluate Davis CoPilot APIs (preview) for natural-language DQL
generation, query explanation, and AI-assisted troubleshooting.
• Define and tune predictive/forecasting use cases using Davis AI (capacity forecasting, anomaly prediction
ahead of customer impact).
• Own Davis AI governance: false-positive tuning, alert noise reduction, and continuous model feedback
loops.
• Translate Davis AI findings into automated Workflow Automation Engine actions (auto-remediation, ticket
creation, Slack/Teams/PagerDuty routing).
MCP Servers & Agentic AI Integration — Core Responsibilities
• Architect and deploy the Dynatrace MCP Server (local/stdio via @dynatrace-oss/dynatrace-mcp-server
and/or the new Remote Dynatrace MCP Server) to connect Dynatrace''s observability platform to AI
coding assistants and agents.
Dynatrace Consultant Architect JD
• Configure secure, governed access using Platform Tokens / OAuth clients with least-privilege scopes
(Grail query permissions, security-problem read access, etc.) for MCP clients.
• Integrate the Dynatrace MCP Server with Claude Code, Claude Desktop, Claude in Chrome/Cowork,
GitHub Copilot (VS Code), Cursor, Amazon Q Developer CLI, and Windsurf to bring live production
context (logs, traces, problems, security events) directly into developer and agentic workflows.
• Enable natural-language-to-DQL workflows via MCP so engineers and AI agents can query Grail (logs,
events, spans, metrics) conversationally.
• Design agentic incident-response patterns: AI agents that fetch problem/vulnerability details, correlate
with recent deployments, and propose remediation — with human-in-the-loop approval for any state
changing action.
• Establish cost governance for MCP-driven Grail queries (query budgets,
DT_GRAIL_QUERY_BUDGET_GB, scoped time windows) since natural-language and agentic queries
can scan large data volumes.
• Monitor and audit MCP tool usage via Grail Business Events (com.dynatrace-oss.mcp.*) — track which
agents/clients connect, which tools they invoke, and error rates.
• Evaluate and pilot dtctl (open-source Dynatrace CLI) alongside the MCP server for agent-driven
dashboard, workflow, and DQL management.
• Build internal enablement/playbooks for engineering teams adopting MCP-connected AI assistants,
including security review of agent permissions.
Supporting Dynatrace Platform Responsibilities
• Design and implement enterprise-wide observability architecture using Dynatrace across AWS
environments (EC2, ECS, EKS, Lambda, Fargate, RDS, DynamoDB, S3, CloudFront, VPC, CloudWatch).
• Configure OneAgent, ActiveGate, Management Zones, tagging standards, Smartscape, and PurePath as the
topology/data foundation Davis AI and MCP tooling depend on.
• Configure Synthetic Monitoring, RUM, Session Replay, DEM, APM, Log Monitoring, and
Security/Runtime Vulnerability Analytics.
• Integrate with ServiceNow, Jira, Slack, Microsoft Teams, PagerDuty, Splunk, Grafana, Prometheus.
• Configure OpenTelemetry, distributed tracing, and Extensions Framework 2.0.
• Implement Configuration as Code (Monaco) and Dynatrace Operator for Kubernetes.
• Define SLOs/SLIs, error budgets, and RBAC/security architecture.
Automation & DevOps
Terraform, CloudFormation, Ansible, Jenkins, GitHub Actions, GitLab CI/CD, Kubernetes, Docker, Helm, Python,
Bash, Node.js (v24+ required for local MCP server), REST APIs.
Required Technical Skills
Dynatrace Consultant Architect JD
Dynatrace, Davis AI, Davis CoPilot, MCP (Model Context Protocol) / Dynatrace MCP Server, Grail (DQL), AWS,
Kubernetes, Docker, Linux, Python, Node.js, Java, REST APIs, Microservices.
Nice to Have
• Prior hands-on experience deploying an MCP server (Dynatrace or otherwise) in a production or enterprise
dev environment.
• Experience with Claude Code, GitHub Copilot, Cursor, or similar AI coding agents in an enterprise setting.
• Dynatrace Professional/Associate Certification, AWS DevOps Engineer Professional, CKA/CKAD.
Soft Skills
• Ability to explain Davis AI causal reasoning and MCP-driven agentic workflows to both engineers and
executive leadership.
• Strong judgment on AI-agent governance — knowing when autonomous action is appropriate vs. requiring
human approval.
• Ability to lead architecture discussions and mentor teams on both classical observability and emerging
agentic-AI integration patterns.
Preferred Experience
• 10+ years IT experience; 5+ years enterprise Dynatrace architecture; 5+ years AWS.
• Direct, hands-on Davis AI tuning experience (not just consuming default anomaly detection).
• Practical experience standing up or integrating an MCP server — ideally the Dynatrace MCP Server —
with at least one AI coding/agent client.
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