Senior AI Engineer in Cambridge

Senior AI Engineer in Cambridge

Cambridge Full-Time 60000 - 80000 £ / year (est.) Home office (partial)
AstraZeneca

At a Glance

  • Tasks: Design and deliver AI systems that transform healthcare challenges into real-world solutions.
  • Company: Join AstraZeneca, a leader in innovative medicine and technology.
  • Benefits: Competitive salary, flexible working, and opportunities for continuous learning.
  • Other info: Collaborative environment with strong career growth potential.
  • Why this job: Make a tangible impact on patients' lives through cutting-edge AI technology.
  • Qualifications: Advanced degree in a relevant field and hands-on AI experience required.

The predicted salary is between 60000 - 80000 £ per year.

Forward Deployed AI, Enterprise AI Unit. Cambridge, UK. Competitive salary and benefits.

Are you ready to turn ambiguous enterprise challenges into credible, production AI that moves the needle for patients and the business? Do you want to ship agentic and retrieval-augmented systems that colleagues rely on every day? Join our forward-deployed AI engineering team within a newly created enterprise unit in Cambridge, where your work will help accelerate how we discover, develop and deliver medicines.

You will be part of a hands-on group that builds with AI and ships AI. We convert scattered data and unstructured knowledge into dependable systems with measurable outcomes. In this role, you will design and deliver end-to-end solutions, lead the rigor that makes demos production-grade, and partner closely with authorities across the company to bring the latest techniques into real-world use.

Based in Cambridge with a competitive salary and benefits, you will have the autonomy to craft solutions from prototype to deployed service, backed by strong platforms, clear sponsorship and a culture that values both ambition and disciplined engineering.

Accountabilities:
  • Agentic AI Development: Design, implement and iterate agentic workflows that orchestrate tools, sub-agents and multi-step reasoning to deliver deterministic, auditable outcomes to business users.
  • Retrieval-Augmented Systems: Build robust RAG pipelines and context engineering strategies that ground LLM outputs in enterprise knowledge, improve factuality and reduce hallucinations.
  • Evaluation and Quality: Establish ground-truth datasets, LLM-as-judge and rule-based evals, and regression suites that separate signal from prompt noise and ensure consistent performance over time.
  • Production Software Engineering: Translate prototypes into reliable services with structured outputs, clear interfaces, packaging, testing and traceability that meet critical software standards.
  • Platform Integration and Observability: Integrate with Kubernetes and cloud platforms, implement CI/CD, and instrument systems for cost, latency, caching and evaluation dashboards to sustain performance at scale.
  • Partner Teamwork: Work with subject-matter experts and product owners to turn ambiguous problems into clear specifications, communicate trade-offs, and land solutions that create business value.
  • Secure and Scalable Deployment: Apply gateway-based inference patterns and led LLM platforms where appropriate to balance security, performance and maintainability.
  • Continuous Innovation: Scan the evolving AI landscape, assess when fine-tuning or distillation is warranted, and proactively upgrade techniques to keep solutions robust and competitive.
Essential Skills/Experience:
  • Advanced University degree in mathematics, computer science or another relevant numerical/computational subject area; alternatively, equivalent experience in relevant research or industry. PhD or equivalent experience desirable.
  • Deep and validated AI foundations — ability to go deep into the maths and nuances of neural networks, transformers, statistics, evaluation methodology — applied with judgement.
  • Hands-on experience designing and shipping production-grade systems built around LLMs: agentic workflows, RAG, tool use, structured outputs and multi-step orchestration.
  • Practical experience with modern agent-integration patterns — MCP, tool/skill ecosystems, agent-to-agent communication, sub-agents and context engineering — and judgement about when to use each.
  • Demonstrated ability to deliver production-grade software using AI coding assistants with the subject area that critical software demands — specs, tests, evals, code review, traceability — not just ad-hoc prototyping.
  • Excellent evaluation skills: designing ground-truth datasets, building LLM-as-judge and rule-based evals, regression suites, calibration and statistical reasoning to distinguish signal from prompt noise.
  • Strong Python — structured-output validation, packaging — and focused testing practice; confident scaffolding a service from prototype to deployed.
  • Excellent problem-solving, collaboration and communication skills; able to translate ambiguous business problems into specs, collaborate with SMEs, and present results to senior stakeholders.
  • Experience with Kubernetes and cloud platforms (Azure and/or AWS), CI/CD, and LLM observability (cost, latency, caching, eval dashboards).
Desirable Skills/Experience:
  • Experience training, fine-tuning or distilling models when the problem genuinely calls for it.
  • Familiarity with managed-LLM platforms (e.g. AWS Bedrock, Azure OpenAI) and gateway-based inference patterns. End-to-end software life cycle and experience shipping and deploying production code.
  • Full-stack delivery experience — comfortable leading a thin frontend when needed; this is not a frontend role and professional UI skills are not required.
  • Interest in working on real-world enterprise problems in a research-driven organisation; pharma or life-sciences experience welcomed but not required.

When we put unexpected teams in the same room, we ignite 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.

Why AstraZeneca:

Here, technology and science meet to change what’s possible for patients. You will join a company investing boldly in digital and data to become truly data-led, where unexpected teams come together to address problems that have never been solved before. We empower engineers to experiment through hackathons and real-world pilots, build at enterprise scale with modern platforms, and learn continuously in a supportive, high-standards culture that values kindness alongside ambition. Your contribution will unlock the potential of our science, streamline how we work, and help bring medicines to people faster.

Step into this role to design and ship AI systems that matter—bring your expertise to Cambridge and help turn pioneering ideas into real-world impact today!

Date Posted: 28-May-2026

Closing Date: 11-Jun-2026

Our mission is to build an inclusive and equitable environment. We want people to feel they belong at AstraZeneca and Alexion, starting with our recruitment process. We welcome and consider applications from all qualified candidates, regardless of characteristics. We offer reasonable adjustments/accommodations to help all candidates to perform at their best. If you have a need for any adjustments/accommodations, please complete the section in the application form.

Senior AI Engineer in Cambridge employer: AstraZeneca

AstraZeneca is an exceptional employer, offering a dynamic work culture in Cambridge that fosters innovation and collaboration. With a strong commitment to employee growth, we provide opportunities for hands-on experience in cutting-edge AI development, supported by competitive salaries and benefits. Our inclusive environment encourages creativity and ambition, making it an ideal place for those looking to make a meaningful impact in the pharmaceutical industry.

AstraZeneca

Contact Details:

AstraZeneca Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Senior AI Engineer in Cambridge

Tip Number 1

Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.

Tip Number 2

Show off your skills! Create a portfolio showcasing your AI projects, especially those that highlight your experience with LLMs and production-grade systems. This will give you an edge and demonstrate your hands-on expertise.

Tip Number 3

Prepare for interviews by brushing up on common AI engineering questions and scenarios. Practice explaining your thought process and how you've tackled complex problems in the past. Confidence is key!

Tip Number 4

Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team at StudySmarter.

We think you need these skills to ace Senior AI Engineer in Cambridge

AI Development
Agentic Workflows
Retrieval-Augmented Systems
Ground-Truth Dataset Design
Production Software Engineering
Kubernetes Integration
Cloud Platforms (Azure, AWS)

Some tips for your application 🫡

Tailor Your Application:Make sure to customise your CV and cover letter to highlight how your skills align with the Senior AI Engineer role. We want to see how your experience in AI development and production-grade systems can make a difference in our team.

Showcase Your Projects:Include specific examples of projects you've worked on that demonstrate your hands-on experience with LLMs and agentic workflows. We love seeing real-world applications of your skills, so don’t hold back!

Be Clear and Concise:When writing your application, keep it straightforward and to the point. We appreciate clarity, so make sure your achievements and experiences are easy to understand and relevant to the role.

Apply Through Our Website:We encourage you to submit your application through our website. It’s the best way for us to receive your details and ensures you’re considered for the position. Plus, it’s super easy!

How to prepare for a job interview at AstraZeneca

Know Your AI Fundamentals

Make sure you brush up on your deep AI foundations, especially around neural networks and transformers. Be ready to discuss how you've applied these concepts in real-world scenarios, as this will show your depth of knowledge and practical experience.

Showcase Your Production Experience

Prepare to talk about specific projects where you've designed and shipped production-grade systems. Highlight your hands-on experience with agentic workflows and retrieval-augmented systems, as well as any challenges you faced and how you overcame them.

Demonstrate Problem-Solving Skills

Be ready to translate ambiguous business problems into clear specifications. Think of examples where you've collaborated with subject-matter experts to deliver solutions that created real business value, and be prepared to discuss the trade-offs you made along the way.

Familiarise Yourself with Tools and Platforms

Since the role involves working with Kubernetes and cloud platforms, make sure you understand CI/CD processes and LLM observability. Bring examples of how you've integrated these technologies in past projects, as this will demonstrate your readiness for the technical demands of the position.