Application Developer

We are seeking talented and motivated individuals to join our Development Team working in the AI Squad within the Development Team. This role is ideal for graduates or early‑career developers with strong programming fundamentals, a genuine enthusiasm for AI, and a curiosity to explore how AI can be applied to solve real‑world problems. 

We’re particularly keen to hear from interested in candidates who demonstrate strong foundational engineering capabilities, alongside evidence of self-driven learning, experimentation, and intellectual curiosity. This may include internships, hackathons, personal or academic projects, or other practical experience that goes beyond academic coursework. 

who have demonstrated their interest through internships, hackathons, personal or academic projects, or relevant coursework, and who show clear evidence of self‑learning and experimentation with modern technologies. 
 
In this role, you’ll collaborate closely with experienced software engineers, data scientists, and data engineers to develop impactful solutions, tackle complex challenges, and support the delivery of AI initiatives across RLAM. You will be supported to learn and grow in a fast-moving, collaborative environment that values curiosity, adaptability, and continuous improvement. 

You’ll be supported to learn, grow, and build your technical and professional skills in a fast‑moving, collaborative, and supportive environment where curiosity and innovation are encouraged.  

About the role  

• Work on real RLAM software products — designing, building, and testing features while applying solid engineering practices, including object-oriented design, clean code principles, and structured application development 

• Build production-quality software, focusing on well-structured, maintainable applications and services  

• Design and integrate with external systems and services, developing an understanding of how modern applications interact and operate 

• Get hands-on with AI and Generative AI, exploring how these techniques can enhance and extend software solutions within real-world applications  

• Collaborate with engineers and data specialists to integrate AI into live systems, working across data pipelines, APIs, and cloud platforms 

• Develop an understanding of how AI solutions are built, evaluated, and monitored within production environments  

• Learn and apply principles of responsible AI, including fairness, explainability, and governance  

• Operate within agile delivery teams — contributing to sprint goals, breaking down work, documenting solutions, and sharing knowledge  

Work effectively in iterative delivery environments, adapting to evolving technologies and requirements within a rapidly advancing AI landscape 

• Work on real RLAM software products — designing, developing, and testing features while learning solid engineering practices, APIs, and cloud‑based integration.  

• Get hands‑on with AI and Generative AI, from building models to experimenting with prompt engineering, fine‑tuning, safety alignment, and early agentic AI concepts.  

• Learn how to build responsible AI by applying fairness, explainability, and governance principles, and by monitoring models to make sure they stay accurate and robust.  

• Collaborate with experienced engineers and data specialists to integrate AI into live systems, helping with data preparation and understanding how cloud and platform services fit together.  

• Work in agile sprints, break down tasks, contribute to documentation, share knowledge, and keep building your skills as you stay up to date with the latest AI and ML developments.  

About you  

Qualification  

• A First-Class/2:1 degree in Computer Science, Artificial Intelligence, Engineering, Mathematics, or a related field  

• We encourage all students to apply, from recent graduates through to MSc/PhD  

Technical Skills  

Solid Strong foundation in software engineering, particularly object‑oriented programming (e.g. Python or C#), alongside an understanding of core software engineering concepts such as data structures, algorithms, and SOLID principles.  

• Demonstrable ability to build structured software applications (e.g. use of classes, modular design) 

• Strong grounding in core programming concepts, including data structures, algorithms, and software design principles (e.g. SOLID) 

• Confident working with SQL to query, manipulate, and analyse structured data, plus familiarity with REST APIs and OpenAPI standards for building and consuming modern services.  

• Exposure to data science and machine learning tools such as PyTorch, scikit‑learn, NumPy, Pandas, Matplotlib, Seaborn, XGBoost, or NLTK, with an interest in applying them to real‑world problems.  

• Growing understanding of modern AI approaches, including LLMs and Retrieval‑Augmented Generation (RAG), and how these models are designed, evaluated, and integrated into applications.  

• Experience (or willingness to learn) unit testing frameworks like PyTest, XUnit, or NUnit to ensure code quality and build good engineering habits early on.  

• Exposure to data science and machine learning tools such as PyTorch, scikit‑learn, NumPy, Pandas, Matplotlib, Seaborn, XGBoost, or NLTK, with an interest in applying them to real‑world problems.  

Desirable  

• Prior internship or work experience in software development, Ddata sScience or AI 

• Experience with AI development tools and platforms (e.g., Azure, Snowflake, AWS, GCP)  

• Awareness Knowledge of CI/CD concepts 

• Exposure to emerging concepts such as Knowledge of Agentic AI  

• Exposure to Git/ and version control practices basics (branching, merging, conflict resolution)  

• Knowledge of CI/CD  

• Evidence of self-learning (projects, GitHub, coursework, hackathons)