This is what you will do:

The 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
• Design and implement efficient development environments and processes for ML applications.
• 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
• Familiarity with API-based application architecture and API frameworks.
• 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