R&D Digital and Architecture intern

Job Overview:

We are looking for a highly motivated and curious AI and MLOps Intern to assist in building and deploying machine learning models and automating AI workflows. This role offers a fantastic opportunity to work closely with data scientists, machine learning engineers, and DevOps teams to streamline the development lifecycle of AI systems and ensure their reliability in production environments.

Key Responsibilities:

• Assist in setting up and optimizing the end-to-end machine learning lifecycle, from data ingestion to model deployment.
• Collaborate with data scientists to automate and manage model training, testing, and validation pipelines.
• Support the deployment of ML models into production using MLOps tools and frameworks like Kubeflow, MLflow, or TFX.
• Help maintain and monitor AI/ML models in production environments to ensure performance, scalability, and reliability.
• Work with cloud platforms (AWS, Azure, or GCP) to manage infrastructure for ML workloads, including GPU/TPU clusters.
• Assist in building automated CI/CD pipelines tailored for machine learning models.
• Contribute to the development of scalable, reusable data pipelines using tools such as Airflow, Kafka, or Spark.
• Explore and implement best practices in AI model versioning, auditing, and security.
• Document processes and provide technical support to teams in areas like model reproducibility, experiment tracking, and model governance.

Required Skills and Qualifications:

• Currently pursuing a Bachelor’s/Master’s degree in Computer Science, Data Science, AI, Machine Learning, or related fields.
• Basic understanding of machine learning concepts and popular ML algorithms.
• Familiarity with Python and common ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Knowledge of version control systems like Git.
• Interest in learning about DevOps principles and how they apply to machine learning.
• Familiarity with Linux/Unix environments and shell scripting.
• Excellent problem-solving skills and the ability to work collaboratively in a team.
• Eagerness to learn new technologies and work with large-scale data systems.

Preferred Qualifications:

• Experience with cloud platforms like AWS, Azure, or GCP for AI/ML workflows.
• Basic understanding of containerization and orchestration tools like Docker and Kubernetes.
• Exposure to MLOps platforms such as Kubeflow, MLflow, TFX, or SageMaker.
• Experience with data engineering tools like Apache Airflow, Kafka, or Spark.
• Knowledge of CI/CD pipelines for AI/ML applications.

Strong understanding of data structures, databases, and ETL processes.

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Alcon is an Equal Opportunity Employer and takes pride in maintaining a diverse environment. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, gender identity, marital status, disability, or any other reason.