Principal AI Systems Engineer

We Are:

At Synopsys,we’retransforming IT operations by embedding intelligence into the core of infrastructure systems. Fromcomputeclusters to observability stacks, we are building agentic AI capabilities that automate operational tasks, enhance system reliability, and accelerate decision-making.

You are a senior AI Systems Engineer with strongexpertisein backend systems, Dev Ops practices, and infrastructure reliability, combined with hands-on experienceinbuilding and deploying modern AI-powered applications. You enjoy working at the intersection of platform engineering and intelligent systems, building production-grade services that integrate AI capabilities into enterprise infrastructure.

You have experience designing scalable APIs, MCPintegrationlayer, and platform integrations that allow

AI agentsand automation frameworks to interact with enterprise systems. You think in terms of reliability, observability, and operational excellence, ensuring systemsoperatereliably in large-scale production environments.

You also have experience with modern AI technologies such as LLM-based applications, retrieval pipelines, or agent frameworks. You understand how AI systems interact with infrastructure signals and enterprise data, and you are comfortable building services that enable intelligent automation and decision-making.

You take ownership of systems end-to-end—from architecture and development to deployment, monitoring, and continuous improvement. You value clean, maintainable code, strong Dev Ops practices, andcollaboratingwith architects, SREs, and platform engineers to deliver reliable intelligent systems.

• Design and implement the MCP integration layer, including MCP registry services and MCP endpoints, enabling AI systems and agents to securely discover and interact with enterprise infrastructure tools and platforms.
• Build scalable backend servicesleveragingexpertisein Python that power automation systems, enterprise infrastructure integrations, and AI-driven operational workflows.
• Develop andmaintainproduction-grade Dev Ops pipelines,including CI/CD workflows, containerized deployments, monitoring, and reliability automation.
• Ensure systems are designed for scalability, reliability, and high availability across both cloud and on-prem enterprise environments supporting AI services.
• Integrate infrastructure signals such as logs, metrics, APIs, and operational data sources into
  
  AI Agentsand automation.
• Collaborate with architects, SREs, and platform teams to design reusable infrastructure integrations and platform services that support enterprise automation and AI capabilities.
• Support intelligent automation initiatives including LLM-powered services, RAG pipelines, and agent-based workflows where applicable.

• Drive the transformation of traditional IT operations through intelligent automation, reducing manualeffortand increasing operational efficiency.
• Shape the next generation of agentic AI services that fundamentally improve infrastructure monitoring, support, and optimization
• Contribute to a culture of innovation by collaborating on new architectures, frameworks, and methodologies for AI-powered IT automation.
• Accelerate incident response and problem resolution by empowering AI agents to analyze, reason, and act on real-time infrastructure data.
• Enhance visibility and decision-making across compute, cloud, and storage environments with data-driven insights generated by intelligent agents.

• 10+ years of experience in software engineering, backend development, infrastructure engineering, or platform systems.
• Strongexpertisein Python and building scalable backend services, APIs, or platform integration layers.
• Experience designing MCP registry services, MCP endpoints, or similar service discovery and integration layers connecting enterprise systems.
• Strong Dev Ops experience,including CI/CD pipelines, containerization (Docker/Kubernetes), infrastructure automation, monitoring, and reliability practices.
• Hands-on familiarity with modern AI systems including LLM-based services, RAG architectures, or agent frameworks.
• Experience working with distributed…