Role Overview:

Are you a hands-on AI practitioner with strong software engineering skills, passionate about building production-grade, agent-enabled AI systems at enterprise scale? We’re looking for someone who enjoys designing and delivering intelligent systems that can reason, plan, use tools, and operate reliably in real-world environments! Join us as we transform how AI is designed, built, and deployed. In this applied role, you’ll focus on delivering agent-based and LLM-enabled AI solutions on the Microsoft stack, including Azure and Microsoft AI Foundry. You will work deeply in code, applying strong software engineering and data science practices to turn complex business problems into safe, governed, scalable AI products that create real-world impact. This is a senior, hands-on individual contributor role, ideal for a Data Scientist (or AI Engineer) who wants to move beyond experimentation and deliver operational AI systems that drive measurable outcomes. You will be part of the central AI team and report to the AI Manager, working alongside engineers, data scientists, and business stakeholders.

Key Responsibilities:

• Design, build and maintain agentic AI solutions, including single and multi-agent systems using LLMs, tools, memory, and workflows.
• Build production-ready AI systems using sound data science and software engineering practices, including high-quality code, APIs, and integrations.
• Develop AI agents capable of reasoning, planning, tool use, and reflection, including interaction via governed tool interfaces and Model Context Protocol (MCP) style patterns.
• Deliver PoCs, pilots, and production solutions on the Microsoft stack (e.g. Azure, Microsoft AI Foundry), from ideation through to operational use.
• Translate complex business problems into agent-enabled solutions, defining success criteria and evaluation approaches and measurable outcomes.
• Contribute to AI safety, governance, and assurance, including evaluation of LLM outputs, monitoring, logging, and human-in-the-loop controls.
• Produce clear technical and non-technical documentation covering architecture, design decisions, trade-offs, risks, and outcomes.

Capabilities and experience required:

• 5+ years’ experience across data science, machine learning, AI and/or software engineering roles.
• Strong hands-on coding experience in Python (and R where appropriate), including use of Git, CI/CD, and modern build and deployment practices.
• Practical experience building LLM-enabled systems, including prompt engineering, RAG, tool calling, MCP-based or equivalent agent-tool integration patterns, and agent frameworks.
• Experience designing and implementing agentic workflows, such as task decomposition, planning, memory management, and orchestration.
• Ability to create clean abstractions and reusable components using sound software design patterns suitable for production environments.
• Strong debugging and problem-solving skills, with the ability to trace complex behaviours across data, models, agents, and external systems.
• Demonstrated ability to translate complex business problems into well-structured AI or analytics solutions, from concept through to delivery.
• Experience building end-to-end AI/ML systems using cloud and data platforms (e.g. Azure ML, VMs, APIs, data stores), including deployment and monitoring.
• Experience working with structured and unstructured data across multiple formats (SQL, CSV, JSON, APIs, documents, logs, etc.).
• Strong documentation and communication skills, able to engage stakeholders with varying levels of technical literacy.
• Ability to independently decompose large, ambiguous problems and deliver outcomes using creativity, experimentation, and sound engineering judgement.
• Tertiary qualifications in Computer Science, Software Engineering, Data Science, Informatics, or a related discipline (or equivalent experience).

Randstad contractor benefits:

• Access to a Udemy license for ongoing learning and continuous professional development
• Access to exclusive discounts across various platforms
• Discounts on private health funds
• 3 payruns a week

At Randstad, we are passionate about providing equal employment opportunities and embracing diversity to the benefit of all. We actively encourage applications from any background.

• Design, build and maintain agentic AI solutions, including single and multi-agent systems using LLMs, tools, memory, and workflows.
• Build production-ready AI systems using sound data science and software engineering practices, including high-quality code, APIs, and integrations.
• Develop AI agents capable of reasoning, planning, tool use, and reflection, including interaction via governed tool interfaces and Model Context Protocol (MCP) style patterns.
• Deliver PoCs, pilots, and production solutions on the Microsoft stack (e.g. Azure, Microsoft AI Foundry), from ideation through to operational use.
• Translate complex business problems into agent-enabled solutions, defining success criteria and evaluation approaches and measurable outcomes.
• Contribute to AI safety, governance, and assurance, including evaluation of LLM outputs, monitoring, logging, and human-in-the-loop controls.
• Produce clear technical and non-technical documentation covering architecture, design decisions, trade-offs, risks, and outcomes.

• Strong hands-on coding experience in Python (and R where appropriate), including use of Git, CI/CD, and modern build and deployment practices.
• Practical experience building LLM-enabled systems, including prompt engineering, RAG, tool calling, MCP-based or equivalent agent-tool integration patterns, and agent frameworks.
• Experience designing and implementing agentic workflows, such as task decomposition, planning, memory management, and orchestration.
• Ability to create clean abstractions and reusable components using sound software design patterns suitable for production environments.
• Strong debugging and problem-solving skills, with the ability to trace complex behaviours across data, models, agents, and external systems.
• Demonstrated ability to translate complex business problems into well-structured AI or analytics solutions, from concept through to delivery.
• Experience building end-to-end AI/ML systems using cloud and data platforms (e.g. Azure ML, VMs, APIs, data stores), including deployment and monitoring.
• Experience working with structured and unstructured data across multiple formats (SQL, CSV, JSON, APIs, documents, logs, etc.).
• Strong documentation and communication skills, able to engage stakeholders with varying levels of technical literacy.
• Ability to independently decompose large, ambiguous problems and deliver outcomes using creativity, experimentation, and sound engineering judgement.

• Tertiary qualifications in Computer Science, Software Engineering, Data Science, Informatics, or a related discipline (or equivalent experience).