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Mid-level AI role in metro with popular title and generalist cloud/LLM demands yields medium competition.
Highly specialized LLM/agent engineering and cloud infrastructure requirements limit cross-industry transferability.
Explicit 5–8 years, 3+ years LLM production, mandatory cloud, infra, and orchestration skills.
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Lead design and development of production-grade agentic AI systems using LLMs, including multi-agent orchestration and RAG pipelines.
Own AI platform components: reusable microservices, agent frameworks, system prompts, and API development across cloud environments (GCP, Azure, AWS).
Provide technical leadership by mentoring junior engineers, shaping AI platform strategy, and collaborating across teams for AI business outcomes.
5-8 years software engineering experience; minimum 3 years focused on LLM-based or AI production systems.
Strong experience with Google's Vertex AI, Azure AI services, and orchestration frameworks like LangGraph or CrewAI.
Proficient in Python and backend development (FastAPI, Go, or Node.js).
Hands-on cloud deployment skills with GCP/Azure services (GKE, Cloud Run) and Infrastructure as Code tools (Terraform/Pulumi).
Senior engineer with deep expertise in agentic AI systems architecture and multi-agent orchestration patterns.
Demonstrated ownership of production-ready, observable, and resilient AI workflows integrating complex enterprise APIs.
Practical experience navigating multiple cloud platforms, balancing reliability, latency, and cost in AI system design.