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.
Key Responsibilities
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
Required Skills & Experience
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
Preferred Qualifications
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
Key Competencies
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
Deliverables
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