AI Data Engineer

We are seeking a highly skilled AI Data Engineer to design, build, and optimise scalable data and AI solutions within a leading banking environment. The ideal candidate will have deep expertise in modern data engineering practices combined with hands-on experience in LLM orchestration frameworks such as LangChain and LangGraph at an enterprise level.

This role will play a critical part in enabling AI-driven decision-making, automation, and advanced analytics across the organisation.

• →Design, develop, and maintain scalable data pipelines and architectures to support AI and machine learning initiatives
• →Build and optimise LLM-powered applications using Frameworks like LangChain and LangGraph amoungs others
• →Develop and manage AI workflows, agents, and orchestration pipelines for enterprise use cases
• →Integrate structured and unstructured data sources into AI-ready data platforms
• →Collaborate with Data Scientists, ML Engineers, and Business Teams to deliver AI-driven solutions
• →Ensure data quality, governance, and compliance within a regulated banking environment
• →Optimise performance of data pipelines and AI systems for scalability and reliability
• →Implement monitoring, logging, and observability for AI systems and pipelines
• →Contribute to data architecture and AI strategy aligned with enterprise roadmaps
• →Stay up to date with emerging trends in AI engineering and data platforms

• 5+ years' experience in Data Engineering or AI Engineering roles
• Strong hands-on experience with LangChain and LangGraph (enterprise-level implementations)
• Proficiency in Python and data engineering frameworks
• Experience building and deploying LLM-based applications and AI agents
• Solid experience with data pipelines (ETL/ELT) and big data technologies
• Experience with cloud platforms (AWS, Azure, or GCP)
• Knowledge of vector databases (e.g., Pinecone, Weaviate, FAISS)
• Experience with API integration and microservices architecture
• Strong understanding of data modelling, data warehousing, and data lakes
• Familiarity with CI/CD pipelines and DevOps practices

• Experience in banking or financial services environments
• Exposure to AI governance, model risk, and compliance frameworks
• Knowledge of MLOps and LLMOps practices
• Experience with streaming technologies (Kafka, Spark Streaming)
• Relevant certifications in Cloud, Data Engineering, or AI/ML

• Strong problem-solving and analytical thinking
• Ability to work in complex, enterprise environments
• Excellent stakeholder engagement and communication skills
• High attention to detail and quality delivery
• Ability to work independently in a contracting environment

• Production-ready AI data pipelines and LLM applications
• Scalable and secure AI architecture implementations
• Documented and governed data and AI workflows
• Continuous improvement of AI platform performance and reliability