Responsibilities:

• Improve Existing AI Applications: Boost the performance, scalability, and robustness of current AI applications, ensuring they match changing business needs.
• Innovation and Development: Contribute towards the design, development, deployment, and maintenance of scalable AI/ML embedded applications, driving operational and commercial success across diverse business use cases.
• Perform User-Centric Testing: Conduct experiments and tests of AI solutions with end users, iterating based on feedback to achieve optimal performance and user satisfaction.
• Finetune and optimise predictive models: Apply state-of-the-art techniques and frameworks to create, train, and evaluate machine learning as well as open-source LLM foundation models
• Build RAG and Agentic pipelines: Develop and implement robust and efficient pipelines for RAG and Agentic frameworks, enhancing the capabilities and performance of our AI solutions.

Education and Experience:

• Bachelor’s degree in computer science, Engineering, Data Science or a related field. Relevant certifications in AI/ML or related fields are highly
• Minimum 4 years of experience in AI/ML engineering or related roles (e.g., data science, software dev)
• Working knowledge in LLMs and Generative AI: Proven experience in developing solutions or proof of concepts with Large Language Models, including familiarity with frameworks such as LangChain, LlamaIndex
• Proficiency in Python: Extensive experience in Python, with a track record of writing production-quality backend code.
• Statistical and ML Knowledge: Good understanding of statistical fundamentals and machine learning theory, with practical application in real-world projects.
• Experience with Application Containerization: Strong experience in application containerization technologies, such as Docker and Kubernetes, ensuring scalability and maintainability.
• Adaptability and Resilience: Ability to excel in a dynamic environment with evolving objectives and user needs, demonstrating flexibility and resilience.
• Cloud Application Development: Experience in developing and deploying applications on Azure.

Advantageous Experience and Skills:

• Experience with MLOps/LLMOps: Knowledge of MLOps/LLMOps practices, enhancing the lifecycle management of machine learning and language model operations.
• Front-End Development Skills: Proficiency in front-end technologies such as JavaScript, React, HTML, and CSS, enabling seamless integration of AI solutions.
• Industry-Specific Experience: Background in insurance, banking services, or a similar field is advantageous, providing relevant context and understanding of industry-specific challenges and opportunities.