At Synopsys, we are transforming IT by building intelligent, agentic systems that think, act, and adapt. Supporting some of the world’s most demanding engineering environments—from chip design to high-performance computing—we are reimagining how infrastructure and operations function through the lens of AI.
Requirements
- 7+ years of experience in AI/ML, software engineering, or intelligent systems, with at least 2 years in LLM-based or agentic AI development.
- Proficiency in Python, with experience building backend systems, APIs, and containerized services.
- Hands-on experience with LLM platforms such as OpenAI, Azure OpenAI, or Anthropic, and techniques like RAG and prompt engineering.
- Familiarity with agent orchestration tools (LangGraph, CrewAI, LangChain, AutoGen, etc.).
- Experience working with infrastructure data (logs, metrics, traces) and integrating with observability or monitoring systems.
- Knowledge of cloud, Linux-based infrastructure, job schedulers (e.g., LSF), or other large-scale systems.
- Workflow automation tools (e.g., Airflow) for connecting agents into end-to-end operational flows.
Responsibilities
- Architect and develop agentic IT capabilities that enhance infrastructure operations, workload orchestration, and user-facing automation.
- Build LLM-powered agents that integrate reasoning, context-awareness, and real-time data from telemetry and operational sources.
- Design orchestration flows using frameworks like LangGraph, CrewAI, or LangChain, enabling agents to collaborate across tasks and systems.
- Implement grounding strategies using retrieval-augmented generation (RAG) tied to internal documentation, logs, metrics, and CMDBs.
- Work closely with infrastructure, observability, and automation teams to embed agents within existing systems and tools.
- Lead design patterns and technical guidance for agent lifecycle management, service reliability, and platform governance.
- Drive the evolution of infrastructure automation from reactive scripting to proactive, intelligent, and adaptive systems.
Other
- A strategic and pragmatic engineer who balances vision with execution.
- A strong communicator who can operate across AI, IT, and infrastructure domains.
- Highly organized and detail-oriented, with the ability to manage complex system interactions.
- A collaborative team player with a passion for solving real problems through intelligent automation.
- Committed to ethical, explainable AI design and responsible deployment practices.
