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Agentic Ai Engineer
Zenotis Infotech
2 hours ago
Full-time
On-site
Burlingame, California, United States
We are looking for an experienced Agentic AI Engineer to build and support production-ready AI systems, LLM infrastructure, and autonomous AI agents.
The ideal candidate will have a strong Software/Backend Engineering background and have transitioned into Generative AI / Agentic AI development.
You will work closely with AI and engineering teams to build scalable, reliable, and high-performance AI platforms.
Key Responsibilities
Build and deploy LLM and Generative AI applications in production.
Develop and scale AI agents and multi-agent workflows.
Work with frameworks such as LangChain, LangGraph, and CrewAI.
Build and optimize LLM serving and inference using tools such as vLLM, Triton, or Ray Serve.
Manage AI workloads using Kubernetes, Docker, and cloud platforms.
Build RAG pipelines, memory systems, and integrations with vector databases.
Implement monitoring, logging, tracing, and evaluation for AI/LLM applications.
Develop CI/CD pipelines and internal tools for deploying AI models and agents.
Troubleshoot performance, scalability, GPU, and production issues.
Required Skills
7–10 years of software, backend, ML, or infrastructure engineering experience.
Strong background in Software/Backend Engineering.
Experience with LLMs / Generative AI / Agentic AI.
Hands-on experience with LangChain, LangGraph, or CrewAI.
Experience with Kubernetes and Docker.
Experience with AWS, GCP, or Azure.
Experience with LLM serving tools such as vLLM, Triton, or Ray Serve.
Strong understanding of distributed systems and scalable infrastructure.
Preferred Skills
Vector databases: Pinecone, Milvus, or Qdrant
RAG and AI memory systems
Model optimization, quantization, LoRA, or KV caching
AI observability tools such as Langfuse, Arize, or Datadog
#J-18808-Ljbffr
The ideal candidate will have a strong Software/Backend Engineering background and have transitioned into Generative AI / Agentic AI development.
You will work closely with AI and engineering teams to build scalable, reliable, and high-performance AI platforms.
Key Responsibilities
Build and deploy LLM and Generative AI applications in production.
Develop and scale AI agents and multi-agent workflows.
Work with frameworks such as LangChain, LangGraph, and CrewAI.
Build and optimize LLM serving and inference using tools such as vLLM, Triton, or Ray Serve.
Manage AI workloads using Kubernetes, Docker, and cloud platforms.
Build RAG pipelines, memory systems, and integrations with vector databases.
Implement monitoring, logging, tracing, and evaluation for AI/LLM applications.
Develop CI/CD pipelines and internal tools for deploying AI models and agents.
Troubleshoot performance, scalability, GPU, and production issues.
Required Skills
7–10 years of software, backend, ML, or infrastructure engineering experience.
Strong background in Software/Backend Engineering.
Experience with LLMs / Generative AI / Agentic AI.
Hands-on experience with LangChain, LangGraph, or CrewAI.
Experience with Kubernetes and Docker.
Experience with AWS, GCP, or Azure.
Experience with LLM serving tools such as vLLM, Triton, or Ray Serve.
Strong understanding of distributed systems and scalable infrastructure.
Preferred Skills
Vector databases: Pinecone, Milvus, or Qdrant
RAG and AI memory systems
Model optimization, quantization, LoRA, or KV caching
AI observability tools such as Langfuse, Arize, or Datadog
#J-18808-Ljbffr