Machine Learning Engineer

Job Description

ABOUT THE ROLE As a skilled Machine Learning Engineer specializing in GenAI and NLP, you will work with a cross functional team responsible for the continued development and scaling of a high-profile Gen AI product at a large biotechnology company. Working with the team you will design, prototype, develop, and optimize AI solutions in support of new feature development and refinement of existing workflows. Key Responsibilities: Contribute to solving business problems with cutting-edge AI solutions and innovate on ways to improve cost-efficiency and reliability of those solutions Collaborate with cross-functional teams to gather requirements and ensure seamless integration with the rest of the ecosystem. Focus on practical AI challenges such as improving reasoning and evaluation in real-world scenarios. Research and stay up-to-date on the latest advancements in generative AI technologies and methodologies. Document architectures, processes, and technical decisions. Experience scaling user-interactive RAG solutions to operate quickly on millions of documents. Design and develop AI tools that can be called via API or through batch transformations for large datasets. Implement end-to-end solutions for batch and real-time algorithms along with tooling around monitoring, logging, automated testing, and performance testing. Desired Qualifications Please note that while you do not need to be an expert in every area, being familiar with most of the following is important. We are looking for someone who can effectively integrate everything, with team support to fill any gaps. Bachelors or Masters degree in Computer Science, Engineering, Data Science, or a related field. Proficiency in basic machine learning concepts and methods, especially those related to GenAI and NLP Strong knowledge of ML/Gen AI services and libraries, such as AWS SageMaker, AWS Bedrock, PyTorch Experience using opensource LLMs Strong knowledge of Python, SQL, and additional data processing languages. Experience in AWS (e.g., EC2, Lambda, RDS) for data processing and back-end applications. Experience with ETL tools (e.g., Apache Spark, Airflow), data modeling/warehousing and CI/CD tools is a plus.