Senior Machine Learning Ops Engineering Lead

**About AstraZeneca**

AstraZeneca is a global, innovation-driven BioPharmaceutical business that focuses on the discovery, development and commercialisation of prescription medicines for some of the world’s most serious disease. But we’re more than one of the world’s leading pharmaceutical companies.

At AstraZeneca we’re dedicated to being a Great Place to Work. Where you are empowered to push the boundaries of science and unleash your entrepreneurial spirit. There’s no better place to make a difference to medicine, patients and society. An inclusive culture that champions diversity and collaboration. Always committed to lifelong learning, growth and development.

**About the project**

**About the role**

In this role you will join a global team of engineers (software, data, MLOps), architects, BAs, PMs, in our Augmented Biologics Platform to support biologics and antibody drug discovery as a **Senior MLOps Engineering Lead**. The following will form part of the role:

- Responsible for productionsing generative and predictive models and support the full model life cylce (code optimisation, deployment, retraining, publishing, monitoring, etc.)
- Responsible for designing, implementing, and integrating AI/ML solutions to make our science faster and more efficient to do, easier to learn from, and offer faster delivery and higher quality across biologics discovery.
- Collaborate with product, design, data science, and our scientific teams to build cutting-edge AI/ML products and services
- Advocate and advance modern, agile MLOps practices and help develop and evangelize a vibrant AI/ML software engineering culture
- Acting as a technical lead, support more junior/less experienced MLOps engineers by mentoring, providing feedback and hands-on career development
- Plan, implement and support core infrastructure development collaboratively with an overall objective to improve the scalability, reliability, performance, and availability of ML services
- Advocate for rigorous engineering practices and discipline: code reviews, automated testing, logging, monitoring, alerting, etc.
- Passionate to stay on top of tech trends in AI/ML, experiment with and learn new technologies, participate in internal & external technology communities, and mentor other members of the engineering community
- Working with cutting edge technology stack in cloud environment

**About you**
- Expertise in Python and ML libraries.
- Solid experience using/building MLOPs platforms at scale covering full model life cycle management (ML pipelines, docker, Kubernetes, REST API based ML inference, model registry, etc.).
- Experience using relational databases (PostgreSQL, Oracle) and NoSQL databases (Elastic Search/ MongoDB).
- Experience of data analysis - profiling, investigating, interpreting, and documenting data structures.
- Experience in performance tuning SQL and understanding ETL pipelines.
- Extensive experience in troubleshooting data issues, analyzing end to end data pipelines, and web service performance.
- Good experience in consuming or exposing web APIs.
- Design, develop, and deploy production-grade scalable AI/ML APIs using container technologies like Docker and Kubernetes.
- Production experience delivering CI/CD pipelines (Jenkins, ArgoCD, TravisCI, Git).
- Technical lead one or more teams delivering data pipelines and MLOps services.
- Experience in creating and critically evaluating MLOps architecture in the cloud.
- Excellent verbal and written skills for effective communication with a variety of personas including, engineers, testers, architects, product managers, scientists, etc.
- Experience working with AWS - advantage.
- Bioinformatics / Computational or molecular biology background.