Position Overview
We are seeking AI Engineers to join the Data Science team to design, build, and deploy GenAI and Agentic AI solutions for cancer research, biometrics, clinical research, and broader R&D workflows.
The role will work closely with data scientists, data integration engineers, and scientific stakeholders to translate complex research needs into practical AI-enabled systems. Example solution areas include LLM-powered workflow automation, RAG-based knowledge retrieval, structured information extraction, AI-assisted data analysis, and decision-support applications.
For senior candidates, the role will also involve leading technical design, guiding solution architecture, mentoring junior team members, and helping move high-value AI use cases from prototype toward production.
Main Duties and Responsibilities
- Design, develop, and deploy GenAI and Agentic AI solutions for scientific research, biometrics, clinical trial, and oncology-related workflows
- Build LLM-powered applications using techniques such as prompt engineering, tool use, structured output generation, RAG, and agentic workflow orchestration
- Integrate AI solutions with existing data platforms, databases, APIs, and enterprise systems
- Collaborate with scientific, clinical, and data stakeholders to understand user needs and translate them into clear technical solutions
- Develop prototypes and MVPs, evaluate their performance, and iterate based on user feedback and business value
- Support appropriate documentation, testing, traceability, and human oversight for AI solutions in a regulated R&D environment
- Stay current with emerging GenAI and Agentic AI technologies and assess their applicability to real-world R&D use cases
- Senior level: Lead technical design decisions, guide architecture choices, mentor junior engineers, and support the transition of selected solutions toward production
Requirements by Level
Education
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Bioinformatics, Statistics, Applied Mathematics, or a related field.
Experience
Junior Level
- 1–3 years of experience in software engineering, data science, machine learning, AI application development, or bioinformatics
- Hands-on experience with Python and practical exposure to LLMs, data pipelines, APIs, or AI-enabled applications
- Strong willingness to learn and apply emerging GenAI technologies in scientific or healthcare-related settings
Senior Level
- 5+ years of experience in AI, machine learning, data science, software engineering, or related fields
- Proven experience delivering AI/ML, GenAI, or data-driven solutions in real-world business, research, or enterprise environments
- Experience leading technical design, making architecture decisions, and guiding projects from problem definition to prototype, evaluation, and deployment
- Ability to mentor junior engineers and collaborate effectively across data science, engineering, and domain expert teams
Essential Technical Skills
- Strong Python programming skills for AI application development, data processing, and workflow automation
- Hands-on experience with LLM-based applications, including prompt engineering, structured outputs, tool use, or agentic workflows
- Experience with RAG architectures, embeddings, vector databases, document processing, or knowledge retrieval systems
- Familiarity with Agentic AI frameworks such as LangChain, LangGraph, Pydantic-AI, CrewAI, or similar tools
- Experience working with REST APIs, SQL databases, and data integration workflows
- Understanding of software engineering best practices, including version control, testing, documentation, and CI/CD
- Familiarity with cloud platforms or enterprise deployment environments, such as AWS, Azure, or GCP
- Basic awareness of data privacy, compliance, validation, and responsible AI considerations in healthcare, life sciences, or other regulated environments
Desirable Skills
- Experience applying AI or data science in clinical research, biometrics, oncology, cancer research, drug development, or healthcare workflows
- Experience designing evaluation approaches for LLM or GenAI systems, including accuracy, retrieval quality, usability, robustness, and human review
- Experience with workflow orchestration, AI agents, automation pipelines, or multi-step decision-support systems
- Familiarity with bioinformatics, genomics, computational biology, computational pathology, or medical imaging use cases
- Experience with TypeScript, JavaScript, or front-end development for building user-facing AI applications
- Understanding of model optimization, fine-tuning, or domain adaptation techniques
- Familiarity with data privacy regulations and compliance expectations such as HIPAA, GDPR, GxP, or internal enterprise governance processes
Personal Attributes
- Strong problem-solving skills and analytical thinking
- Ability to work independently while collaborating effectively in cross-functional teams
- Strong communication skills and ability to explain technical concepts to non-technical stakeholders
- Curiosity and eagerness to learn emerging AI technologies
- Attention to detail and commitment to building reliable, usable, and well-documented solutions
- Passion for applying AI to advance cancer research, clinical development, and patient outcomes
Date Posted
08-5月-2026
Closing Date
30-7月-2026
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 authorization and employment eligibility verification requirements.
