Lead AI Engineer

Introduction to role:
Are you ready to disrupt an industry and change lives? The Lead AI Engineer will develop and deploy key AI products, generating business and scientific insights through advanced data science techniques. This role involves building models using both foundational and cutting-edge methods, processing structured and unstructured data, and collaborating closely with internal stakeholders to solve complex problems in drug development, manufacturing, and supply chain.

Accountabilities:
- Drive the implementation of advanced modelling algorithms to generate actionable business insights.
- Mentor AI scientists, plan and supervise technical work, and collaborate with stakeholders.
- Work within an agile framework and cross-functional teams to align AI solutions with business goals.
- Engage internal stakeholders and external partners for successful delivery of AI solutions.
- Continuously monitor and optimize AI models for accuracy and efficiency.
- Document processes, models, and key learnings while contributing to building internal AI capabilities.
- Ensure AI models adhere to ethical standards, privacy regulations, and fairness guidelines.

Essential Skills/Experience:
- Bachelor's in operations research, mathematics, computer science, or related quantitative field.
- Advanced expertise in Python and familiarity with database systems (e.g., SQL, NoSQL, Graph).
- Proven proficiency in at least 3 of the following domains:
  - Generative AI: advanced expertise working with LLMs/transformer models, AWS Bedrock, SageMaker, LangChain
  - Computer Vision: image classification and object detection
  - MLOps: putting models into production in the AWS ecosystem
  - Optimization: production scheduling, planning
  - Traditional ML: time series analysis, unsupervised anomaly detection, analysis of high dimensional data
- Proficiency in ML libraries sklearn, pandas, TensorFlow/PyTorch
- Experience productionizing ML/Gen AI services and working with complex datasets.
- Strong understanding of software development, algorithms, optimization, and scaling.
- Excellent communication and business analysis skills.

Desirable Skills/Experience:
- Master’s or PhD in a relevant quantitative field
- Cloud engineering experience (AWS cloud services)
- Snowflake
- Software development experience (e.g., React JS, Node JS)

When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.

At AstraZeneca, our work has a direct impact on patients by transforming our ability to develop life-changing medicines. We empower the business to perform at its peak by combining cutting-edge science with leading digital technology platforms and data. With a passion for impacting lives through data, analytics, AI, machine learning, and more, we are at a crucial stage of our journey to become a digital and data-led enterprise. Here you can innovate, take ownership, and run with it—experimenting with leading-edge technology in a dynamic environment that offers countless opportunities to learn and grow.

Ready to make a meaningful impact? Apply now to join us on this exciting journey!

Date Posted

Closing Date

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.