
AI Agents Engineer
Zendesk
full-time
Posted on:
Location Type: Remote
Location: Portugal
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Tech Stack
About the role
- Contribute to the design and development of robust, stateful, and scalable AI agents using Python and modern agentic frameworks (e.g., LangChain, LlamaIndex)
- Support the evaluation and selection of appropriate foundation models and services from third-party providers (e.g., OpenAI, Anthropic, Google)
- Collaborate closely with your immediate team, including product managers and software engineers
- Troubleshoot and debug AI systems to ensure optimal performance and reliability in production environments for assigned components
- Document development processes, code, and findings to ensure knowledge sharing and maintainability within the team
Requirements
- Bachelor’s or Master’s in a relevant field (e.g., Computer Science, AI, Machine Learning, NLP)
- Familiarity with LLM-Oriented System Design: Understanding of multi-step, tool-using agents (e.g., LangChain, Autogen)
- Basic understanding of prompt engineering, context management, and LLM behavior (e.g., hallucinations)
- Ability to integrate agents with external tools, databases, and APIs (e.g., OpenAI, Anthropic) in secure execution environments
- Understanding of RAG pipelines with vector databases
- Basic understanding of LLM evaluation frameworks and monitoring for latency and accuracy
- Awareness of prompt injection and basic concepts of implementing guardrails and fallback strategies
- Basic understanding of managing LLM token budgets and latency
- Familiarity with concepts of agents with long-term memory and planning capabilities
- Proficient in Python (Fast API) and LLM SDKs
Benefits
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Applicant Tracking System Keywords
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Hard Skills & Tools
PythonLangChainLlamaIndexLLM-Oriented System Designprompt engineeringcontext managementRAG pipelinesvector databasesFast APILLM SDKs
Soft Skills
collaborationtroubleshootingdebuggingdocumentationknowledge sharing
Certifications
Bachelor’s in Computer ScienceMaster’s in AIMaster’s in Machine LearningMaster’s in NLP