PSSM ML/AI Engineer Job in Chennai at Pfizer

Pfizer is hiring for the role of PSSM ML/AI Engineer!

Responsibilities:

• Collaborate with partner development lines and data science team members to comprehend PSSM business needs and translate them into technical requirements for applying machine learning solutions.
• Preprocess and clean large PharmSci datasets for model training and evaluation, ensuring data quality and addressing potential biases.
• Design, develop, and implement machine learning frameworks (e.g., TensorFlow, PyTorch, sci-kit-learn) to PSSM business workflows.
• Incorporating MLOps tools and best practices to support the integration of ML models into production systems, monitor and maintain models, ensuring high availability and performance.
• Continuously improve machine learning pipelines by automating tasks and optimizing workflows.
• Document machine learning projects and technical decisions clearly and concisely. Communicate results with stakeholders to enhance business decisions and/or influence strategic direction.
• Stay abreast with advancements in machine learning research for diverse environments (cloud and edge). Identify opportunities for enhanced performance and innovation.
• Support upskilling the organization on the application of ML/ AI and to help build a continuous learning culture.

Requirements:

• Master's degree in Computer Science (with a focus on ML), Mathematics, or a related field (or equivalent experience) with 3+ years’ experience. PhD degree is a plus.
• Solid understanding of machine learning algorithms and concepts (e.g., supervised learning, unsupervised learning, deep learning networks, efficient architectures for edge devices).
• Strong programming skills in Python with experience in both machine learning libraries (e.g., TensorFlow, PyTorch).
• Experience with data preprocessing, cleaning, and feature engineering techniques.
• Experience with model evaluation metrics and techniques for model selection and hyperparameter tuning.
• Experience with cloud platforms (e.g., AWS, GCP, Azure) for managing deployments across cloud and edge is a plus.
• Familiarity with containerization technologies like Docker and Kubernetes is a plus.
• Familiarity with MLOps tools and concepts (e.g., CI/CD for ML, model monitoring) is a plus.
• Experience with building and deploying large-scale machine learning systems is a plus.