Behavioural Finance
AI Engineer / Agentic Workflow Engineer (Python)
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Purpose of the role
We are looking for you to join our team and to help design and implement Python-based agentic workflows on Microsoft Azure. You will work closely with the internal team to turn business problems into reliable, testable, production-ready AI workflows using Azure AI services, LLMs, tool/function calling, retrieval, orchestration, and monitoring.
Core responsibilities
- Design and implement agentic workflows using Python and Azure AI services.
- Build LLM-powered workflows that can call tools, interact with APIs, retrieve relevant information, execute multi-step tasks, and handle failures gracefully.
- Work with internal stakeholders to clarify requirements, break down ambiguous problems, and propose practical technical solutions.
- Implement clean, maintainable Python code with appropriate tests, logging, error handling, and documentation.
- Integrate AI workflows with internal systems, data sources, APIs, and cloud services.
- Design evaluation approaches for agentic workflows, including test cases, success criteria, traces, regression checks, and quality measures.
- Support deployment, monitoring, governance, and responsible use of AI workflows on Azure.
- Keep the team informed about relevant developments in LLMs, agentic AI, Azure AI Foundry, and practical AI engineering patterns.
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Essential skills
- Strong Python software engineering experience, including production-quality code, testing, packaging, APIs, and debugging.
- Hands-on experience building LLM or agentic AI applications beyond simple chatbot prototypes.
- Experience with tool/function calling, structured outputs, retrieval-augmented generation, prompt design, agent orchestration, and workflow automation.
- Strong problem-solving ability and comfort working with ambiguous requirements.
- Practical Azure experience, ideally including Azure AI Foundry, Azure OpenAI, Azure Functions, Azure Container Apps, Azure Storage, Key Vault, identity/access management, and monitoring.
- Ability to design reliable systems with appropriate guardrails, retries, fallbacks, observability, and human-in-the-loop controls.
- Understanding of security, data governance, responsible AI, and safe integration of LLMs with business systems.
- Clear communication skills and ability to work collaboratively with a small internal team.
Desirable skills
- Experience with Azure AI Foundry Agent Service, Microsoft Agent Framework, Semantic Kernel, LangGraph, AutoGen, or similar orchestration frameworks.
- Experience with MCP servers/tools and agent-tool architecture.
- Experience with Azure AI Search, vector search, document processing, or knowledge-base integration.
- Experience with CI/CD, Docker, GitHub Actions, Azure DevOps, or infrastructure-as-code.
- Experience designing evaluation datasets and automated quality checks for LLM workflows.
- Familiarity with cost, latency, reliability, and monitoring trade-offs in production AI systems.
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Preferred background
The ideal candidate is an AI engineer or senior Python software engineer with a strong software engineering foundation and recent hands-on experience building production LLM or agentic AI systems. A pure data science background is less important than the ability to build robust, maintainable, cloud-deployed AI workflows.
Example evidence we would like to see
- A previous project involving LLM agents, function calling, tool use, RAG, or workflow automation.
- Code samples or architecture examples showing clean Python engineering.
- Experience deploying AI workflows into a cloud environment.
- Evidence of thinking about evaluation, reliability, monitoring, and security rather than only prompt design.
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