Overview
Skills
Job Details
Job Summary:
Join a dynamic team to drive end-to-end machine learning (ML) projects from design to deployment and continuous improvement.
Key Responsibilities:
Collaborate with data scientists on advanced ML techniques: supervised, reinforcement learning, deep learning, and GenAI.
Work with researchers to integrate innovative strategies that enhance investor outcomes.
Design and build scalable ML platforms using AWS services like SageMaker, Lambda, Glue, Step Functions, EMR, and S3.
Develop and optimize predictive models using TensorFlow, PyTorch, PySpark, and Pandas.
Evaluate models rigorously and perform hyperparameter tuning and performance monitoring.
Build and manage data/model pipelines for ML workflows in distributed computing environments.
Apply AWS security best practices including IAM, VPC, and bucket policies.
Contribute to ML Ops, from data collection to monitoring and retraining models.
Support automated data ingestion and perform raw data analysis for model development.
Use software design patterns to create modular, scalable codebases.
Translate business needs into analytical solutions by working with cross-functional teams.
Engage in pipeline and model design reviews with data scientists and engineers.
Resolve issues flagged by monitoring tools in production environments.
Participate in ongoing business planning and prioritization processes.
Keep up-to-date with the latest ML advancements and AWS tools.
Support special projects and contribute to a culture of continuous improvement.
Qualifications:
Bachelor s degree or equivalent experience.
5 7 years of experience in ML, software development, and Python frameworks.
3 5 years of hands-on AWS experience in cloud-native ML solutions.
Passion for building impactful, secure, and scalable machine learning systems.