AI Principal Engineer / Developer

About the Role We are looking for an experienced and innovative AI Principal Engineer / Developer to lead the design, development, and implementation of advanced AI and Machine Learning (ML) solutions. In this role, you will be responsible for building and maintaining scalable data pipelines and backend architectures that power AI/ML workflows. You will play a key role in developing Large Language Model (LLM) applications, Retrieval-Augmented Generation (RAG) solutions, and other intelligent systems that drive real business value. This position combines strategic technical leadership with hands-on development, offering the opportunity to shape our AI capabilities from the ground up. Key Responsibilities Design, build, and maintain data pipelines and backend systems to support AI/ML applications. Develop and deliver LLM-based solutions, integrating them into production-ready systems. Architect and deploy RAG frameworks, vector search, and embedding-based solutions. Collaborate with cross-functional teams, including data scientists, ML engineers, and software developers, to deliver scalable, efficient AI solutions. Write clean, maintainable, and efficient Python code following best engineering practices. Design and optimise data structures and workflows across relational (RDBMS) and non-relational databases. Manage data migration, optimisation, and performance tuning across systems. Implement robust version control and CI/CD processes using Git and modern DevOps tools. Provide technical guidance, mentorship, and leadership within the AI engineering team. Research and evaluate emerging AI technologies and frameworks to enhance development efficiency and solution quality. Key Requirements Proven experience developing and delivering LLM applications in production environments. Strong expertise in RAG systems, vector databases, and embedding techniques. Advanced proficiency in Python , with demonstrable experience writing clean, maintainable, and efficient code. Strong knowledge of data engineering, database technologies, and data warehouse platforms such as Snowflake. Proficiency with Git and other version control tools. In-depth understanding of AI/ML workflows, data architecture, and backend development. Excellent analytical, problem-solving, and communication skills. Ability to balance strategic planning with hands-on execution. Experience with cloud computing platforms (AWS, Azure, or GCP). Familiarity with MLOps, CI/CD pipelines, and containerization (Docker, Kubernetes). Experience with data orchestration tools such as Airflow or Prefect. Background in software architecture and technical leadership within AI-focused teams.