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Tier-1 brand, metro location, mid-level experience, and strong AI/backend skill demand increase competition.
Specialized LLM/agent engineering and cloud deployment skills create high domain specificity and limited cross-industry fit.
Explicit 2–4 years plus mandatory Python, LangGraph/Gemini, GCP, containers and Terraform create strict screening filters.
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Develop and maintain stateful multi-agent AI workflows using LangGraph to automate complex business tasks.
Implement and optimize large language model (LLM) interactions via Gemini Enterprise, focusing on prompt engineering and structured outputs.
Build production-grade Python APIs and services deployed on GCP with containerization and infrastructure as code, ensuring performance, security, and operational excellence.
2–4 years professional software engineering experience, strong command of Python including asynchronous programming.
Experience with AI orchestration frameworks like LangGraph or similar (e.g., LangChain).
Hands-on experience with frontier LLMs such as Gemini or GPT-4, including prompt versioning and managing token limitations.
Familiarity with cloud-native architectures, RESTful APIs, and deployment using Docker/Kubernetes and Terraform on GCP.
Proven ability to independently take AI workflows from prototype to production with minimal oversight.
Experienced in building and managing distributed, stateful AI systems integrating human-in-the-loop checkpoints.
Comfortable working cross-functionally to deliver complex AI solutions using cutting-edge Google AI technology and agentic orchestration.