Agentic Artificial Intelligence Engineer

Role Summary

Key Responsibilities

• Architect and build multi-agent systems using LangGraph, LangChain or equivalent orchestration frameworks
• Implement LLM tool-calling, planning loops, state management and multi-step task execution
• Develop and maintain MCP servers and custom tool integrations
• Build retrieval pipelines (RAG) using vector stores and structured data sources
• Connect agents to enterprise systems — ERP, databases, internal APIs and third-party services
• Work with business stakeholders to translate manual processes into automated agentic workflows
• Ensure secure handling of company data within all AI systems
• Implement evaluation frameworks, regression testing and quality benchmarks for agent behaviour
• Build observability: tracing, logging, error handling, retry logic and fallback paths
• Monitor and optimise latency, token consumption and operating cost at scale
• Define and enforce guardrails, access controls and human-in-the-loop checkpoints
• Take initiatives from concept to production deployment and ongoing support

Required Qualifications

• Bachelor’s degree in Computer Science, Software Engineering or a related field
• 3+ years of professional software engineering experience with expert-level Python
• 1–2 years of hands-on experience building agentic AI systems — multi-agent architectures, tool-calling and autonomous workflows deployed in a production or live business environment
• Demonstrable experience with LLM APIs (Anthropic, OpenAI, Azure OpenAI or equivalent)
• Strong working knowledge of RAG, embeddings, vector databases and context management
• Experience with REST APIs, relational databases and system integration
• Proficiency with Git, CI/CD pipelines and containerised deployment (Docker)
• Practical understanding of prompt engineering, agent evaluation and failure-mode debugging