Data & AI Accelerators MLOps Engineer

AstraZeneca is an innovation-driven global biopharmaceutical company focused on the discovery, development, and commercialization of prescription medicines, primarily for the treatment of cardiovascular, renal and metabolic, respiratory and immunological, oncological, and rare diseases.

Our Barcelona hub has been created to achieve the next wave of breakthroughs that help us do what’s never been done before, as we expand our global footprint. Harnessing data and using the best technology solutions available allow us to positively impact science.

In line with our firm commitment to training and developing exceptional talent, this person will join our Data & AI Accelerators Platform to learn and grow through active collaboration with our team. The mission of this platform is to provide ML/AI engineering support to R&D to reduce solution delivery time while ensuring adherence to best practices and compliance with governance frameworks.

As a junior/mid-level MLOps Engineer, you will play a key role in operationalizing machine learning (ML) models and data pipelines that power critical R&D efforts. Working closely with data scientists, platform engineers, and domain experts, you will help design, build, and maintain robust and scalable ML infrastructure in a highly regulated and data-rich environment. This is an exciting opportunity to grow your MLOps skillset while making a tangible impact in the biomedical research domain.

Accountabilities

1. Develop and Maintain ML Pipelines
- Work with data scientists to build, automate, and optimize ML workflows using tools such as Kubernetes/Kubeflow and Domino Data Lab.
- Ensure end-to-end reproducibility and traceability of models, from data ingestion to deployment.

2. Cloud Infrastructure & Automation
- Implement best practices for cloud-based DevOps on AWS, including provisioning, monitoring, and scaling resources.
- Collaborate with the Cloud & DevOps team to integrate CI/CD pipelines for seamless model versioning and deployment.

3. Model Experimentation & Monitoring
- Use Weights & Biases (or similar platforms) for experiment tracking, hyperparameter tuning, and model evaluation.
- Develop dashboards and alerts to monitor model performance and proactively address issues in production.

4. Collaboration & Knowledge Sharing
- Leverage GitHub Enterprise for code management, pull requests, and collaboration.
- Utilize JIRA/Confluence to document processes, share insights, and coordinate tasks across cross-functional teams.

5. Biomedical Research Domain Support
- Partner with scientists and researchers to understand data requirements, domain-specific constraints, and regulatory considerations.
- Contribute to solutions that enhance data quality, ensure compliance, and accelerate research workflows in a regulated environment (e.g., GxP).

6. Continuous Improvement
- Stay current with industry trends in MLOps, DevOps, and ML frameworks.
- Identify opportunities for tooling, automation, and process improvements that increase reliability and reduce time-to-market for ML solutions.

Essential Skills/Experience

Education & Experience
  - Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
  - 1–3 years of experience in software engineering, DevOps, or data engineering. Exposure to ML pipelines or MLOps is a plus.
Technical Skills
  - Proficient in Python for scripting and data manipulation.
  - Familiarity with AWS services (EC2, S3, Lambda, EKS, etc.).
  - Infrastructure as code technologies such as Terraform scripting.
  - Experience with container orchestration (e.g., Kubernetes, Kubeflow) is strongly preferred.
  - Exposure to ML experiment tracking (e.g., Weights & Biases, MLflow) and CI/CD tools.
  - Knowledge of Git-based version control systems (e.g., GitHub Enterprise).
  - Basic R knowledge for statistical computing and data visualization is a plus.
Domain Knowledge
  - Experience in biomedical research or healthcare data (internships or projects) is a strong advantage.
  - Understanding of data privacy and regulatory compliance in the life sciences is a plus.
Soft Skills
  - Strong problem-solving and communication skills.
  - Ability to work collaboratively in an Agile, fast-paced environment.
  - Eagerness to learn, adapt, and take on new challenges.

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 offers an environment where you can connect across the whole business to power each function to better influence patient outcomes and improve their lives. You will play an increasingly crucial role in driving disruptive transformation on our journey to becoming a digital and data-led enterprise. Unleash the power of our latest innovations in data, machine learning, and technology to turn complex information into life-changing insights.

Ready to make a difference? Apply now!

Date Posted

Closing Date

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.