• Drive the full lifecycle development — from design to deployment — of scalable chatbots and virtual assistants utilizing Microsoft Bot Framework, Kore.ai, or similar CAI technologies • Collaborate with cross-functional teams (product managers, designers, UX/UI specialists) to gather requirements, design conversational flows, and implement user stories • Integrate Natural Language Processing (NLP) and Natural Language Understanding (NLU) techniques to enable chatbots to understand user intent and respond accordingly • Leverage Generative AI services to enhance chatbot responses with creativity and personalization • Develop chatbots for deployment across multiple channels (web, mobile apps, messaging platforms) ensuring a seamless user experience • Conduct thorough testing and debugging to ensure high performance and accuracy of conversational AI solutions • Monitor and analyze user interactions and chatbot performance to identify areas for improvement and optimization • Stay updated on the latest advancements in Conversational AI technologies and best practices

• Design, implement, and optimize the data infrastructure that supports AI and machine learning (ML) solutions • Ensure data availability, quality, and security • Ingest and transform data for the data lake and Feature Store • Develop and support the operationalization of AI and ML models • Conduct complex analyses and monitor data quality

• Provide technical leadership to more junior members of the Applied AI Engineering team; • Identify opportunities to apply AI to drive business impact; • Perform exploratory data analysis and data visualization to understand business needs and inform LLM applications; • Develop patterns and practices to improve the scalability of LLM applications; • Optimize the Applied AI Engineering development stack (CI/CD, evaluation of libraries, etc.); • Develop AI applications using vector databases, LLMs, single-agent and multi-agent architectures; • Build validation pipelines for LLM applications, including datasets and using LLMs as a judge; • Develop small back-end and front-end components to validate proofs of concept for LLM applications; • Actively research technical blogs, academic papers and video conferences to generate improvement and optimization hypotheses for LLM applications;

• Build and optimise LLM-powered systems • Architect, fine-tune and optimise machine learning models including LLMs and classical ML approaches • Experiment with modern architectures such as Transformers and Mixture-of-Experts • Apply efficient training and fine-tuning techniques including LoRA and parameter-efficient methods • Develop robust evaluation approaches to measure model quality and reliability • Develop scalable AI systems • Build and deploy production-grade NLP pipelines and services • Ensure models operate efficiently with low latency and high availability • Integrate models into scalable cloud infrastructure and real-world applications • Lead the acquisition, processing and governance of large structured and unstructured datasets • Apply exploratory data analysis and validation techniques to improve training pipelines • Ensure data privacy and governance standards suitable for financial services • Contribute to DevOps and MLOps pipelines • Implement robust version control, testing and CI/CD workflows • Support reliable deployment and monitoring of models in production environments • Mentor engineers and contribute to the technical growth of the team • Work closely with product, engineering and data teams to deliver AI solutions • Stay at the forefront of AI and NLP research, applying new approaches where they add value