Role overview
- Design and develop AI-driven solutions for analysing complex regulatory content
- Build and orchestrate AI agents capable of reasoning, planning, and autonomous task execution
- Implement retrieval-augmented generation (RAG) pipelines and knowledge grounding
- Integrate AI solutions with enterprise data sources and internal systems
- Deploy and monitor AI solutions in production environments using MLOps/LLMOps practices
- Evaluate LLM and agent performance and ensure reliability and scalability
- Transform AI outputs into structured insights that deliver business value
Basic qualifications
- Several years of hands-on experience in applied ML or AI engineering
- Strong Python programming skills
- Experience with AI platforms such as Azure OpenAI, Google Vertex AI, Amazon Bedrock, or similar
- Practical experience building AI agents (tool use, reasoning workflows, planning loops, autonomous execution)
- Familiarity with orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, or similar
- Experience implementing RAG pipelines and enterprise integrations
- Knowledge of deploying AI systems to production and working with cloud ML ecosystems (Azure, AWS, GCP)
- Experience handling complex, high-volume, multi-source data
- Strong communication skills and ability to translate technical outputs into business value
- Professional proficiency in English
Preferred qualifications
- Experience with scikit-learn, pandas, PyTorch, or TensorFlow
- Understanding of deep learning and classical ML techniques
- Opportunity to build impactful AI solutions in a regulated, high-stakes environment
- Work on cutting-edge agentic AI and enterprise AI integration
- Collaborate with multidisciplinary teams delivering strategic value
- Influence compliance efficiency and risk reduction through AI innovation
Tags & focus areas
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