Sr. Manager, Mlops Engineer
hace 2 semanas
Location: Barcelona, Spain
Job reference: R-194366
Date posted: 03/11/2024
**Sr. Manager, MLOps Engineer**
Office based in Barcelona.
**This is what you will do**:
The Senior Manager MLOps Engineer reports to the IT Director of Insights and Analytics and is a critical role in Alexion IT RDU organization. You will be a key member of our IT team and play a crucial role in developing and implementing innovative machine learning solutions for our business. Your expertise in MLOps will be critical in designing, building, and deploying production-ready machine learning models at scale.
**You will be responsible for**:
- Leading the development and implementation of MLOps infrastructure and tools for machine learning models
- Collaborating with cross-functional teams to identify, prioritize and solve business problems using machine learning techniques
- Designing, developing and implementing production-grade machine learning models that meet business requirements
- Overseeing the training, testing, and validation of machine learning models
- Ensuring that machine learning models meet high-quality standards, including scalability, maintainability, and performance
- Communicating with stakeholders and senior management to provide updates on the progress of machine learning projects
- Developing assets, accelerators and thought capital for your practice by providing best in class framework and reusable components
- Developing and maintaining MLOps pipelines to automate machine learning workflows and integrating them with existing IT systems
- Integrating Generative AI models based solutions within the broader machine learning ecosystem, ensuring they adhere to ethical guidelines and serve the intended business purposes
- Implementing robust monitoring and governance mechanisms for Generative AI models based solutions to ensure they evolve in alignment with business needs and regulatory standards
**You will need to have**:
- Bachelor's degree in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related field
- 4+ years of experience in developing and deploying machine learning models in production environments
- Hands-on experience building production models with focus on data science operations including serverless architectures, Kubernetes, Docker/containerization, and model upkeep and maintenance
- Experience with CICD orchestration frameworks, such as GitHub Actions, Jenkins or Bitbucket pipelines.
- Deep understanding of software development lifecycle and maintenance.
- Extensive experience with one or more orchestration tools (e.g Airflow, Flyte, Kubeflow)
- Experience working with MLOps tools like experiment tracking, model registry tools and feature stores (e.g MLFlow, Sagemaker, Azure)
- Strong programming skills in Python and experience with libraries such as Tensorflow, Keras, or PyTorch
- Proficiency in MLOps best practices, including model training, testing, deployment, and monitoring
- Experience with cloud computing platforms, such as AWS, Azure or GCP
- Strong understanding of software engineering best practices and agile methodologies
- Strong understanding of data structures, algorithms, and machine learning techniques
- Excellent communication and collaboration skills with the ability to work in a cross-functional team environment
- Ability to work independently and self-driven with strong problem-solving skills
- Excellent communication and collaboration skills with the ability to partner well with business stakeholders.
**We would prefer for you to have**:
- Experience in the pharmaceutical industry or related fields
- Advanced degree in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related field
- Strong understanding of parallelization and asynchronous computation.
- Strong knowledge of data science techniques and tools, including statistical analysis, data visualization, and SQL
AstraZeneca requires all US employees to be fully vaccinated for COVID-19 but will consider requests for reasonable accommodations as required by applicable law.
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