COMPETITIVE SALARY & EXCELLENT COMPANY BENEFITS

On average, it takes more than 10 years to develop a drug and cost more than $1.3b. Over 70% of drug R&D cost is spending on clinical development and yet the success rate from phase I to approval is only around 10%. At Evinova, a new health tech business as part of the AstraZeneca Group, we are under a mission to increase clinical trial success rate by 20%, accelerate clinical development timeline by 36 months and reduce study cost by 50%, by using state of the art AI and Machine Learning.
If you are a solid coder with hands on experience of developing AI agentic solutions, good understanding of modern deep learning, strong AWS skills, and a voracious learner then you could be a fantastic fit for our team.
Talent with ambition to grow into a role will be a key differentiator of a successful candidate. Glass ceiling smashers especially welcome.
We are looking for a Director of AI Engineering to lead our AI and ML development. The role is a technical expert driving hands-on development from prototyping to production ready systems. The role will focus on the design of complex AI agents, communication architectures for agents, and developing automated techniques for designing and evaluating agentic systems. On top of AI agentic systems, this role will also oversee traditional deep learning model development from design, training, evaluating to deployment, with a focusing on fine turn LLMs. The role will interact with product, design, engineering, MLops, and domain experts and partners.
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.

SKILLS AND CAPABILITIES NEEDED

Qualifications:

• Ph.D. or equivalent experience in a relevant field (such as mathematics, computer science, data science, etc).
• 5+ years of proven experience in applied machine learning, with a strong focus on deep learning, NLP, and generative AI
• Proven track record of developing creative and novel AI solutions that have driven significant business impact.
• Extensive prior experience exploring and testing large language model behaviour, prompting and building products with language models.
• Expert knowledge of Python and advanced ML/LLM frameworks (e.g., TensorFlow, PyTorch, LangChain, LlamaIndex, etc).
• Deep understanding of agentic AI concepts and frameworks (e.g., agentic design patterns, multi-agent systems, reinforcement learning) and their applications in healthcare.
• Previous experience of training (fine turn) large language models, hands on experience with DeepSpeed
• Extensive experience with AWS services (e.g., SageMaker, Bedrock, MSK, EKS, OpenSearch).
• Proven record of shipping production level code with best software engineering practice
• Experience with containerization technologies, CI/CD, front and backend of web applications

• Excellent verbal and written communication skills with experience presenting to executive leadership and partners.

• Demonstrated ability to lead and inspire multi-functional teams.

Desirable Skills/Experience

• Experience with TypeScript
• Experience with AWS CDK
• Demonstrated technical leadership experience, including successful delivery of large-scale AI projects.
• Experience designing and implementing novel AI architectures or algorithms in real-world products.
• Experience with low-level languages used for implementing high-performance ML code (C/C++, Rust, CUDA, etc.).
• Contributions to open-source AI projects or development of proprietary AI frameworks.
• Expertise in areas such as reinforcement learning, few-shot learning, meta-learning, Causal AI.

Knowledge of drug development and previously experience of working in pharmaceutical industrial is nice to have but not required.
Why Evinova (AstraZeneca)?
Evinova draws on AstraZeneca’s deep experience developing novel therapeutics, informed by insights from thousands of patients and clinical researchers. Together, we can accelerate the delivery of life-changing medicines, improve the design and delivery of clinical trials for better patient experiences and outcomes, and think more holistically about patient care before, during, and after treatment. We know that regulators, healthcare professionals, and care teams at clinical trial sites do not want a fragmented approach. They do not want a future where every pharmaceutical company provides its own, different digital solutions. They want solutions that work across the sector, simplify their workload, and benefit patients broadly. By bringing our solutions to the wider healthcare community, we can help build more unified approaches to how we all develop and deploy digital technologies, better serving our teams, physicians, and ultimately patients. Evinova represents a unique opportunity to deliver meaningful outcomes with digital and AI to serve the wider healthcare community and create new standards for the sector. Join us on our journey of building a new kind of health tech business to reset expectations of what a bio-pharmaceutical company can be. This means we’re opening new ways to work, pioneering cutting-edge methods, and bringing unexpected teams together. Interested? Come and join our journey.
So, what’s next?
Are you already imagining yourself joining our team? Good, because we can’t wait to hear from you.
Closing Date for Applications: 1st June

This is a hand on role, expect a minimal of 80%-time spending on coding

You will:

• Lead the design, development, and deployment of sophisticated agentic AI applications tailored to life sciences/health tech challenges.
• Develop automated techniques for designing and evaluating agentic systems
• Ideate, develop, and evaluate different tools for agents (e.g., search, memory, context compression, communication architectures for agents).
• Design specialized agent/LLM observability pipelines for prototypes and production systems.
• Develop bespoke deep learning models, from model design, training, testing all the way to deployment.
• Fine tune LLM.
• Mentor and develop the technical capabilities of the AI team, fostering a culture of innovation and continuous learning, led by example with best ML and software engineering practice.
• Lead a portfolio of high-impact AI projects.
• Drive technical and project-level architecture decisions, guiding multi-functional teams through stages of the AI lifecycle.
• Deliver production ready code.
• Collaborate in a multidisciplinary environment to align AI initiatives with business objectives and drive digital transformation
• Represent the company’s AI expertise at conferences, publications, and industry events.