Z
Zenotis Group
Agentic Ai Engineer
Skills
Google Cloud PlatformRustDockerPythonGolangDatadogMachine LearningAIAWSKubernetesTerraformAzure
12 days ago
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Important information
Contract type:
Permanent
Location:
Burlingame, CA, USA
Work mode:
Onsite
Published on:
14 October 2026
What they need
- 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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