Machine Learning Engineer

We are seeking a production-focused Machine Learning Engineer (MLE). Unlike a traditional Data Science role, this position prioritizes the engineering, deployment, and scalability of machine learning systems. You will be responsible for moving models from research to production, ensuring they are robust, integrated into our cloud infrastructure, and compliant with industry standards.
Key Responsibilities
· MLOps on AWS: Lead the end-to-end MLOps lifecycle within the AWS ecosystem, with a focus on CI/CD for machine learning and automated model monitoring.
· Infrastructure & Data Modeling: Design and implement scalable infrastructure architecture, including complex data models specifically structured for ML workloads.
· ML Pipelines: Build and maintain automated pipelines to handle data ingestion, preprocessing, training, and deployment at scale.
· Feature Engineering: Develop and optimize sophisticated feature engineering workflows to enhance model accuracy and operational efficiency.
· Regulatory & Data Engineering: Bridge the gap between data engineering and model deployment while adhering to strict regulatory requirements inherent to the life sciences industry.
Technical Qualifications
Category Requirements
Experience Proven track record as an MLE with experience productionizing models (rather than just DS/Analytics).
Cloud Platform Expert knowledge of AWS (SageMaker, Lambda, Glue) for ML applications.
Data Engineering Strong background in data engineering, ETL design, and data modeling.
Domain
Knowledge Experience in the Healthcare or Life Sciences sector.
Compliance Understanding of regulatory experience and working within regulated data environments.