About us

Billigence Pty Ltd is a specialist in the delivery of market-leading Business Intelligence and CRM solutions. Headquartered in Sydney, Australia and with offices in Prague, London, Frankfurt and Singapore our passion is data and our focus is the delivery of end-to-end solutions via a talented team of skilled professionals.

We are partners with leading edge software platforms including Snowflake, dbt, Tableau, Alteryx, Collibra, and Salesforce.

What we are looking for:

We are looking for an Agentic AI Developer to design, build, and deploy intelligent, autonomous AI systems capable of reasoning, planning, and executing complex tasks with minimal human intervention. This role focuses on leveraging large language models (LLMs) and multi-agent architectures to create scalable, real-world AI applications.

Key Responsibilities

Design and develop agent-based AI systems that can autonomously perform multi-step tasks
Build and optimize workflows using large language models (LLMs)
Implement prompt engineering strategies to improve model accuracy, reliability, and efficiency
Develop and manage multi-agent orchestration frameworks for complex task execution
Integrate AI systems into production environments and real-world applications
Collaborate with cross-functional teams to identify use cases and translate them into AI-driven solutions
Monitor, evaluate, and improve system performance, scalability, and robustness
Stay up to date with emerging trends in agentic AI, LLM tooling, and orchestration frameworks

Essential experience required:

Strong proficiency in Python and experience building production-grade applications
Solid understanding of LLMs and their capabilities, limitations, and optimization techniques
Experience with prompt engineering and evaluation methods
Hands-on experience building or working with autonomous agents or agent frameworks
Familiarity with APIs, microservices architecture, and cloud environments
Strong problem-solving skills and ability to design scalable systems

Preferred Qualifications

Experience with Model Context Protocol (MCP) or similar standards
Experience with multi-agent orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI, or similar)
Knowledge of vector databases, embeddings, and retrieval-augmented generation (RAG)
Experience integrating AI into real-world products or enterprise systems
Familiarity with MLOps practices, monitoring, and evaluation pipelines
Understanding of security, safety, and ethical considerations in AI systems

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