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You will play a key role in a regulatory submission content automation initiative which will modernize and digitize the regulatory submission process, positioning Amgen as a leader in regulatory innovation. The initiative leverages state-of-the-art technologies, including Generative AI, Structured Content Management, and integrated data to automate the creation, review, and approval of regulatory content. We are seeking a highly skilled Machine Learning Engineer with a strong MLOps background and experience working with Large Language Models (LLMs) to join our team. You will play a pivotal role in building and scaling our machine learning models and LLM applications from development to production. Your expertise in both machine learning and operations will be essential in creating efficient and reliable pipelines and applications.
Job Responsibility:
Develop and deploy applications that utilize LLMs such as OpenAI GPT 4, Claude, Gemini
Build and maintain MLOps pipelines, including data ingestion, versioning, chunking, vectorization, feature engineering, model training, deployment, and monitoring
Leverage cloud platforms (AWS, GCP, Azure) for ML model development, training, and deployment
Implement DevOps/MLOps/LLMOps best practices to automate ML workflows and improve efficiency
Develop and implement monitoring systems to track model performance and identify issues
Conduct A/B testing and experimentation to optimize model performance
Work closely with data scientists, engineers, and product teams to deliver ML solutions
Stay updated with the latest trends and advancements
Requirements:
a Master's degree and 1-3 years of experience in Software Engineering, Data Science or Machine Learning Engineering preferred OR a Bachelor's degree and 3 to 5 years of Software Engineering, Data Science or Machine Learning Engineering OR a Diploma and 7 to 9 years of Software Engineering, Data Science or Machine Learning Engineering experience
Python
TensorFlow
PyTorch
Scikit-learn
langchain
Familiarity with AWS, Azure, or Google Cloud
excellent analytical and troubleshooting skills
strong verbal and written communication skills
ability to work effectively with global, virtual teams
high degree of initiative and self-motivation
ability to manage multiple priorities successfully
team-oriented, with a focus on achieving team goals
strong presentation and public speaking skills
Nice to have:
Experience in building custom solutions using LLMs to meet specific business needs
Experience in DevOps tools (e.g., Docker, Kubernetes, CI/CD)
Experience with data engineering and pipeline development