SUMMARY OF THE ROLE
As an AI Algorithm Scientist within the Enterprise Architecture team of Commercial IT, you will play a key role in advancing AstraZeneca’s Enterprise AI Platform and accelerating AI adoption across the organization. You will transform emerging AI technologies into scalable, production-ready capabilities that enable business innovation and operational excellence.
Working at the intersection of AI research and enterprise delivery, you will evaluate and apply state-of-the-art AI technologies—including Large Language Models (LLMs), AI Agents, Prompt Engineering, Fine-tuning, and Multimodal AI—to solve real-world business challenges. You will design and develop reusable AI Skills and enterprise AI components that encapsulate best practices, domain knowledge, workflows, and tools into standardized capabilities that can be leveraged across multiple use cases.
You will also contribute to enterprise-specific model development and optimization, support legacy AI and machine learning solutions where required, and collaborate closely with architects, engineers, product teams, and business stakeholders to deliver secure, scalable, and responsible AI solutions from concept to production. Through continuous technology exploration and hands-on implementation, you will help shape the future of Enterprise AI at AstraZeneca.
ROLE & RESPONSIBILITIES
- Design, develop, and deliver AI-powered solutions on AstraZeneca’s Enterprise AI Platform, transforming business requirements into secure, scalable, and production-ready AI applications.
- Design, develop, and maintain reusable AI Skills (standardized capabilities for enterprise AI agents), AI Agents, and enterprise AI components that encapsulate business workflows, domain knowledge, best practices, and tool integrations to accelerate AI adoption across the organization.
- Build and optimize enterprise AI applications by leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, Prompt Engineering, Fine-tuning, and Multimodal AI to solve complex business challenges.
- Collaborate closely with Enterprise Architects, Product Managers, Software Engineers, Data Scientists, and business stakeholders to translate business needs into scalable AI solutions and enterprise platform capabilities.
- Apply modern AI engineering and software engineering best practices to build reusable, maintainable, testable, and production-grade AI services, ensuring high quality, scalability, and long-term sustainability.
- Integrate AI capabilities with enterprise systems, APIs, business applications, knowledge repositories, and data platforms to deliver end-to-end intelligent business solutions.
- Continuously improve AI solution quality through prompt optimization, retrieval enhancement, agent orchestration, model evaluation, latency optimization, cost optimization, and overall system performance tuning.
- Be responsible for enterprise-specific AI model training and optimization, including techniques such as supervised fine-tuning (SFT), knowledge distillation, reinforcement learning from human feedback (RLHF), and model quantization/compression, to continuously improve model accuracy, performance, and efficiency.
- Evaluate and introduce emerging AI technologies including new foundation models, agent frameworks, orchestration technologies, and AI engineering approaches, assessing their applicability and business value within AstraZeneca’s Enterprise AI ecosystem.
- Support the modernization of existing AI, Natural Language Processing (NLP), and classical Machine Learning solutions by incorporating Generative AI technologies where appropriate.
- Contribute to Enterprise AI governance by promoting reusable architecture, engineering standards, AI evaluation methodologies, documentation, security, responsible AI principles, and platform best practices.
- Support broader Commercial IT initiatives by providing technical expertise in enterprise application development, system integration, architecture design, and solution delivery beyond AI-specific projects, as business needs require.
REQUIREMENTS
- Bachelor’s degree or above in Computer Science, Artificial Intelligence, Software Engineering, Data Science, or a related technical discipline. Master’s degree is preferred.
- 5+ years of experience in AI engineering, Machine Learning, NLP, or software engineering, including at least 2 years of hands-on experience building enterprise Generative AI or Large Language Model (LLM) applications.
- Strong hands-on experience with modern AI technologies, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, Prompt Engineering, Fine-tuning, Embedding Models, Model Evaluation, and Multimodal AI.
- Familiarity with model training and optimization techniques such as supervised fine-tuning (SFT), knowledge distillation, reinforcement learning from human feedback (RLHF), and model quantization/compression, with hands-on experience in at least one of these techniques.
- Experience building enterprise AI applications using one or more AI orchestration frameworks.
- Strong Python programming skills and experience building production-grade AI applications using modern software engineering practices, including API development, testing, version control, CI/CD, and deployment.
- Experience integrating AI applications with enterprise systems, APIs, databases, knowledge repositories, and business platforms, with a solid understanding of enterprise architecture principles and scalable solution design.
- Experience optimizing AI application performance through prompt engineering, retrieval optimization, model selection, agent orchestration, evaluation methodologies, latency optimization, and cost-performance optimization.
- Familiarity with traditional Machine Learning and Natural Language Processing techniques, with the ability to maintain or modernize existing AI solutions where appropriate.
- Experience with cloud or hybrid enterprise AI platforms (e.g., Azure, AWS, Alibaba Cloud) is highly desirable.
- Strong analytical thinking and problem-solving skills with the ability to translate complex business challenges into practical AI solutions while balancing technical feasibility, business value, security, and Responsible AI considerations.
- Excellent communication and stakeholder management skills, with the ability to collaborate effectively across Enterprise Architecture, Product Management, Engineering, business teams, and external technology partners.
- Strong written and verbal communication skills in both English and Chinese.
- Self-motivated, proactive, and adaptable, with a passion for emerging AI technologies and continuous learning in a rapidly evolving AI landscape.
AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorisation and employment eligibility verification requirements.
