Lead Ml/ai Operations Engineer

Are you ready to be part of the future of healthcare? Are you able to think big, be bold and harness the power of digital and AI to tackle longstanding life sciences challenges? Then Evinova, a new healthtech business part of 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.

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 a newly formed team of battle-tested and proven SaaS ML/AI developers and operators that will spearhead the design, creation, and operational excellence of our entire ML/AI data and computational AWS ecosystem to catalyze and accelerate science-led innovations through our pharmaceutical clinical SaaS products.

This team is responsible and accountable 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, and ensure best-in-class operations - striving to push even beyond mere compliance with industry standards such as Good Clinical Practices (GCP) and Good Machine Learning Practice (GMLP).

On the human side of the equation, our team forges deep relationships across broader engineering, design, product and science organizations and are quintessential and consummate interdisciplinary teammates and collaborators that can build on our deep heritage within global pharmaceutical clinical trials to drive State-of-the-art (SOTA) AI product experiences with tangible and quantifiable product and business operations impact.

As a Lead 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-focused and interdisciplinary teams/culture founded on trust, mutual respect, growth mindsets, and an obsession for building extraordinary products with extraordinary people.
- Lead by example in using reactive firefighting to drive 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 mitigation 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 pipelines and models 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