Agentic AI Engineer

Zenotis Infotech · Burlingame, CA

Spotted 3h agofulltime
Job description

About this role

Employer-provided description, formatted for easier reading.

Agentic AI Engineer

Location: Burlingame, CA – Hybrid

Job Type: Full-Time

Experience: 7–10 Years

About the Role

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 Python programming skills.
  • 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
  • GPU/TPU infrastructure
  • Terraform or Pulumi
  • Go or Rust
  • LLMOps / AgentOps
  • Model optimization, quantization, LoRA, or KV caching
  • AI observability tools such as Langfuse, Arize, or Datadog
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