AI Engineer

• Design, develop and maintain scalable Python applications and data pipelines using Pandas for data extraction, transformation and loading (ETL).

• Build and expose secure, well-documented REST APIs using FastAPI, applying data validation (e.g. Pydantic) and API best practices.

• Write efficient SQL and work with relational databases such as Oracle, PostgreSQL and MS SQL Server, including data modelling, query optimization and performance tuning.

• Containerize applications using Docker and support deployment across development, test and production environments.

• Develop Generative AI and Agentic AI solutions – LLM-based applications, RAG pipelines, and multi-agent workflows using frameworks such as LangChain, LangGraph or CrewAI.

• Integrate Large Language Models through provider APIs (e.g. OpenAI / Azure OpenAI) and open-source models (e.g. Hugging Face), applying prompt engineering, structured outputs and function / tool calling.

• Build natural-language interfaces to data (e.g. Text-to-SQL agents) that allow business users to query databases in plain English.

• Automate business workflows by combining AI capabilities with APIs, scheduling / workflow tools (e.g. n8n) and messaging integrations.

• Integrate AI solutions with enterprise data sources and tools, including vector databases and Model Context Protocol (MCP) based tool integrations.

• Implement LLM observability, tracing and evaluation (e.g. Langfuse) to monitor quality, cost, latency and reliability of AI applications.

• Develop, train and evaluate Machine Learning models where appropriate, using libraries such as Scikit-learn, TensorFlow and NumPy.

• Follow sound engineering practices: version control with Git, code reviews, unit / integration testing and clear technical documentation.

• Contribute to CI/CD pipelines (e.g. GitHub Actions / Jenkins) and support deployment of AI services on on-premise or cloud platforms (AWS / GCP / Azure).

• Translate business requirements into technical designs and work closely with business users, data teams and other stakeholders throughout implementation and delivery.

Contributing Responsibilities

• Contribute to the design of reusable components, frameworks and best practices for data engineering and AI development within the team.

• Ensure solutions comply with the Group’s standards on data security, privacy, Responsible AI, risk and compliance.

• Identify opportunities for automation and process improvement, and propose AI-driven solutions.

• Participate in knowledge sharing, mentoring and cross-training to build AI capabilities within the team.

• Stay up to date with the evolving AI / LLM ecosystem and assess new tools and techniques for adoption.