Lead AI & Automation Engineer (Team) - 19791
Huy Tran
HR Administrator
Efterfrågade kompetenser
Om tjänsten
Role Summary
We are seeking a hands-on AI & Automation Technical Lead to define and deliver AI-driven automation capabilities across Data Center, Network, Cybersecurity and End User Services. The assignment combines technical leadership with active engineering responsibility: owning the roadmap, solution architecture and production delivery while guiding a team of engineers.
The mission is to build secure, governed and production-ready AI assistants, agents, self-service workflows and automation solutions. These capabilities will reduce manual operational effort and ticket volumes, improve recovery and self-service, strengthen operational security, and generate measurable efficiency and cost savings.
Key Responsibilities
- Own the technical roadmap, architecture and end-to-end delivery of AI and automation initiatives across infrastructure and enterprise service domains.
- Lead engineers while remaining actively involved in the design, development and deployment of production-ready solutions.
- Develop AI assistants, AI agents, self-service workflows and automation solutions using Generative AI, Agentic AI, LLMs, RAG and modern orchestration frameworks.
- Integrate enterprise platforms across infrastructure, security, workplace and service management environments.
- Drive AIOps capabilities, including intelligent incident management, event correlation, observability, self-healing workflows and automated remediation.
- Take proof-of-concepts through secure, supportable and operationally sustainable production implementation.
- Implement governance controls for AI solutions, including guardrails, role-based access control, data protection, human approval processes, auditability and cost optimization.
- Communicate complex technical concepts, architecture decisions and delivery priorities clearly to business and technical stakeholders.
Technical Stack & Requirements
- Generative AI, Agentic AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP) and agent-to-agent (A2A) integration patterns.
- Azure AI Foundry, Azure OpenAI, Microsoft Copilot, ServiceNow and Ansible.
- Python and PowerShell for automation and integration development.
- REST APIs, Git, Infrastructure as Code (IaC), Terraform and CI/CD pipelines.
- Cloud-native technologies and enterprise platform integration.
- AIOps, observability, Site Reliability Engineering (SRE) and FinOps practices are advantageous.
Qualifications & Experience
The ideal candidate has a strong background in enterprise automation, AI engineering, platform engineering or infrastructure engineering, together with demonstrated technical leadership experience. Proven ability to move proof-of-concepts into secure, supportable production services is essential. A degree in Computer Science, IT, Engineering or equivalent practical experience is required.
Relevant Microsoft Azure, AI, Security, Microsoft 365 or Power Platform certifications are advantageous. Azure AI engineering capability, such as AI-103, AI-300 or equivalent, is preferred, as are Red Hat Ansible and/or ServiceNow certifications. Experience with ITIL, enterprise architecture, AIOps, SRE and FinOps is beneficial.