As a Principal Machine Learning Engineer, you will build and model digital product and platforms that bring Amgen’s AI/ML and GenAI solutions to life.

This role has to collaborate with Amgen’s Technical Architect, Product Manager, UX designers, and Back-end engineers to design secure, scalable, and user-centric products that accelerate discovery, manufacturing, and commercial analytics, corporate functions products

Key Responsibilities

• Strong experience with statistics and machine learning, including deep learning, natural language processing (NLP) and, experience in building cloud-scale systems and working with open-source stacks for data
• Experiment with large language models (LLM), Artificial intelligence (AI) for code or related fields, Generative AI, Foundational Models, Supervised and Unsupervised Learning
• Collaborate with cross-functional teams to understand the requirement and design solutions that meet business needs. Also with Data Architects, Business SMEs, and Technical Architect to design and develop  to meet fast-paced business needs.
• Explore new tools and technologies that will help rapid development of solutions
• Participate in sprint planning meetings and provide estimations on technical implementation 
• Embed responsible-AI and security-by-design controls

Required Qualifications

• 11 –15 years of experience in Data Science/ML  (eg: managing structured and unstructured data, applying statistical techniques and reporting results)
• Doctorate in Computer Science, Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science or a related field 
• Leverage cloud platforms (AWS preferred) to build scalable and efficient solutions 
• Strong background in Deep Learning, Machine Learning, NLP, Data Mining.
• Excellent communication and stakeholder management skills.

Preferred Skills

• Experience in Generative AI, Foundational Models, LLM's, Feature Engineering, Selection & Extraction, BI & Automation, Predictive Modelling, Data Visualization, CNN, RNN, GNN, Transformers, Exploratory Data Analysis
• Familiarity with Python packages (Pytorch, TensorFlow, Hugging FaceScikit-learn, Pandas, NumPy, Matplotlib, Cloud Vision API, RAG, TensorBoard, OpenCV, NLTK)), programming languages ( C, C++, Java, CUDA,SQL, NoSQL, PHP, HTML, JS, CSS etc.)
• Prior exposure to pharma / life sciences AI environments is preferred.
• Strong understanding of Responsible AI and model validation principles.