AI & Automation Architect (Team) - 19788
Huy Tran
HR Administrator
Required Skills
Job Overview
Role Summary
An experienced AI & Automation Architect is needed to lead end-to-end enterprise architecture, translating business needs into production-ready AI, GenAI, automation and infrastructure solutions. The assignment covers target architecture, standards, reference patterns, technology roadmaps, platform and model selection, data integration, security and governance. The architect will guide engineering teams from discovery and proof of concept through deployment, operations, lifecycle management and adoption, while remaining hands-on with prototyping, technology evaluations, architecture reviews and code reviews.
The role spans enterprise AI and automation across cloud, hybrid cloud, data center, networking, security, workplace technologies and service management. The consultant will design scalable LLM and agentic AI solutions, establish operational patterns for AIOps and SRE, and ensure that AI platforms are secure, observable, governed and production-grade.
Key Responsibilities
- Own the target architecture, reference patterns, standards and technology roadmap for AI, GenAI, automation and enterprise infrastructure capabilities.
- Design and deliver production-grade AI, GenAI and automation platforms from business case and architecture through engineering, production deployment and user adoption.
- Guide engineering teams through discovery, proof of concept, implementation, operations and lifecycle management.
- Lead architecture and design for LLM, GenAI and agentic systems, including model evaluation, fine-tuning, routing, monitoring and knowledge grounding.
- Design single-agent and multi-agent solutions using tool and function calling, MCP/A2A integration, controlled automation, human approval workflows and observability.
- Define automation, AIOps and reliability engineering patterns, including telemetry, remediation, infrastructure automation, APIs, GitOps and CI/CD delivery practices.
- Conduct hands-on prototyping, technical evaluations, architecture reviews and code reviews.
- Establish AI security, governance and operational controls covering RBAC, data protection, guardrails, auditability, human oversight, model quality, versioning, monitoring, FinOps and continuous improvement.
Technical Stack & Requirements
- AI and GenAI: LLMs, SLMs, multimodal and foundation models, fine-tuning, RAG, Agentic RAG, embeddings, vector databases, semantic search, knowledge graphs, AI agents, MCP/A2A and AI evaluation.
- Microsoft AI and automation ecosystem: Microsoft Copilot, Copilot Studio, Microsoft Foundry, Azure OpenAI, Power Platform, Azure Functions, Logic Apps and Azure API Management.
- Automation and platform engineering: Ansible, Python, PowerShell, Bash, Terraform, OpenTofu, APIs, Git, GitOps and CI/CD.
- Observability and reliability: OpenTelemetry, AIOps, SRE, SLI/SLO, error budgets, event management, autonomous remediation and monitoring.
- Infrastructure and hybrid cloud: VMware, Hyper-V, Azure Local/HCI, Windows, Linux, Active Directory, storage, backup, disaster recovery and Cohesity.
- Network and security: Aruba, Palo Alto, SD-WAN, VXLAN/EVPN, Zero Trust, NAC, SASE/SSE, SIEM, SOAR and NDR.
- Workplace and endpoint technologies: Microsoft 365, Intune, Autopilot, Entra ID, Microsoft Defender, Citrix/EUC and DEX.
- Service management and analytics: ServiceNow ITSM, ITOM and CMDB, along with Power BI.
Qualifications & Experience
The ideal consultant has 15+ years of experience in enterprise architecture, infrastructure, cloud, automation, software engineering or related technical roles, with substantial hands-on delivery experience. Proven experience designing and delivering enterprise AI, GenAI or automation platforms beyond strategy and proof-of-concept work is required. The role requires deep understanding of network, security, data center, workplace and cloud operations, combined with architecture governance capability.
Preferred qualifications include Microsoft Azure Architect, AI Engineer, DevOps or Security certifications; AI/ML, GenAI or cloud AI credentials; Terraform and/or Red Hat Ansible Automation certification; VMware, Aruba or Palo Alto professional certifications; TOGAF or an equivalent enterprise architecture qualification; and ITIL, ServiceNow, SRE or cloud architecture credentials.