MACHINE LEARNING/AI OPERATIONS ENGINEER - EVINOVA

Introduction to role.
Are you ready to be part of the future of healthcare? Can you think big, be bold, and harness the power of digital and AI to tackle longstanding life sciences challenges? Then Evinova, a new healthtech business within the AstraZeneca Group, might be for you! Transform billions of patients’ lives through technology, data, and cutting-edge ways of working.
You’re disruptive, decisive, and transformative—someone who’s excited to use technology to improve patients’ health.
We’re building Evinova, a fully-owned subsidiary of AstraZeneca Group, to deliver market-leading digital health solutions that are science-based, evidence-led, and human experience-driven.
Smart risks and quick decisions come together to accelerate innovation across the life sciences sector.
Be part of a diverse team that pushes the boundaries of science by digitally empowering a deeper understanding of the patients we’re helping.
Launch game-changing digital solutions that improve the patient experience and deliver better health outcomes.
Together, we have the opportunity to combine deep scientific expertise with digital and artificial intelligence to serve the wider healthcare community and create new standards across the sector.
Accountabilities.
The Machine Learning and Artificial Intelligence Operations team (ML/AI Ops) is newly formed to spearhead the design, creation, and operational excellence of our entire ML/AI data and computational AWS ecosystem to catalyze and accelerate science-led innovations.
This team is responsible for the design, implementation, deployment, health, and performance of all algorithms, models, ML/AI operations (MLOps, AIOps, and LLMOps), and Data Science Platform.
We manage ML/AI and broader cloud resources, automating operations through infrastructure-as-code and CI/CD pipelines, ensuring best-in-class operations—striving to push beyond mere compliance with industry standards such as Good Clinical Practices (GCP) and Good Machine Learning Practice (GMLP).
As a ML/AI Operations Engineer for clinical trial design, planning, and operational optimization on our team, you will lead the development and management of MLOps systems for our trial management and optimization SaaS product.
You will collaborate closely with data scientists to transition projects from embryonic research into production-grade AI capabilities, utilizing advanced tools and frameworks to optimize model deployment, governance, and infrastructure performance.
This position requires a deep understanding of cloud-native ML/AI Ops methodologies and technologies, AWS infrastructure, and the unique demands of regulated industries, making it a cornerstone of our success in delivering impactful solutions to the pharmaceutical industry.
Role & Team Key Responsibilities.
Operational Excellence   Lead by example in creating high-performance, mission- foc used and interdisciplinary teams /culture founded on trust, mutual respect, growth mindsets, and an obsession for building extraordinary products with extraordinary people.
D rive the creation of proactive capability and process enhancements that ensures enduring value creation and analytic compounding interest.
Design and implement resilient cloud ML/AI operational capabilities to maximize our system A- bilities (Learnability, Flexibility, Extendibility, Interoperability, Scalability).
Drive precision and systemic cost efficiency, optimized system performance, and risk mi t igation with a data-driven strategy, comprehensive analytics, and predictive capabilities at the tree-and-forest level of our ML/AI systems, workloads and processes.
ML/AI Cloud Operations and Engineering   Develop and manage MLOps /AIOps/ LLMOps systems for clinical trial design, planning and operational optimization.
Partner closely with data scientists to shepherd projects from embryonic research stages into production-grade ML/ AI capabilities.
Leverage and teach modern tools, libraries, frameworks and best practices to design, validate, deploy and monitor data pipeline s and model s in production (examples include, but are not limited to AWS Sagemaker, MLflow, CML, Airflow, DVC, Weights and Biases, FastAPI, Litserve, Deepchecks, Evidently, Fiddler, Manifold).
Establish systems and protocols for entire model development lifecycle across a diverse set of algorithms, conventional statistical models, ML and AI/ GenAI models to ensure best-in-class Machine Learning Practice (MLP).
Enhance system scalability, reliability, and performance through effective infrastructure and process management.
Ensure that any prediction we make is backed by deep exploratory data analysis and evidence, interpretable, explainable, safe, and actionable.
Personal Attributes.
Customer-obsessed and passionate about building products that solve real-world problems.
Highly organized and detail-oriented, with the ability to manage multiple initiatives and deadlines.
Collaborative and inclusive, fostering a positive team culture where creativity and innovation thrive.
Essential Skills/Experience.
- Deep understanding of the Data Science Lifecycle (DSLC) and the ability to shepherd data science projects from inception to production within the platform architecture.
- Expert in MLflow, SageMaker, Kubeflow or Argo, DVC, Weights and Biases, and other relevant platforms.
- Strong software engineering abilities in Python/JavaScript/TypeScript.
- Expert in AWS services and containerization technologies like Docker and Kubernetes.
- Experience with LLMOps frameworks such as LlamaIndex and LangChain.
- Ability to collaborate effectively with engineering, design, product, and science teams.
- Strong written and verbal communication skills for reporting and documentation.
- Minimum of 4 years in ML/AI operations engineering roles.
- Proven track record of deploying algorithms and machine learning models into production environments.
- Demonstrated ability to work closely with cross-functional teams, particularly data scientists.
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.
AstraZeneca is where creativity meets critical thinking! We embrace technology to reimagine healthcare's future by predicting, preventing, and treating conditions more effectively.
Our inclusive approach fosters collaboration internally and externally to share diverse perspectives.
We empower our teams with trust and space to explore innovative solutions that redefine patient experiences across their journey.
Join us as we drive change that benefits both business and patients.
Ready to make an impact? Apply now to join our journey towards transforming healthcare! Date Posted 30-jun- Closing Date 14-jul- 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.