JPMorgan Machine Learning Software Engineer-Associate 

United States, Ohio 

113510557

As Machine Learning Software Engineer, you will apply your depth of knowledge and expertise to all aspects of the Machine Learning software development lifecycle, as well as partner continuously with your many stakeholders on a daily basis to stay focused on common goals. We embrace a culture of experimentation and constantly strive for improvement and learning.

Job responsibilities

• Deploy machine learning models in production environments using MLOps best practices

• Implement and optimize ML pipelines for training, evaluation, and inference on distributed systems in AWS cloud (SageMaker, ECS)

• Architect and develop data processing workflows to handle large-scale datasets for machine learning applications

• Build and maintain scalable ML infrastructure with monitoring, versioning, and automated retraining capabilities

• Develop robust model evaluation frameworks to measure and track model performance metrics

• Implement A/B testing frameworks for ML model deployment and validation

• Collaborate with data scientists to translate research prototypes into production-ready ML systems

Required qualifications, capabilities, and skills

• Proven experience (3+ years) as a Machine Learning Engineer with a track record of deploying ML models to production

• Expert-level experience in ML development lifecycle including data preparation, feature engineering, model training, evaluation, and deployment

• Advanced proficiency in Python for ML development using frameworks such as TensorFlow, PyTorch, scikit-learn, or similar

• Demonstrated experience implementing and optimizing ML pipelines in AWS SageMaker, including model training, hyperparameter tuning, and deployment

• Hands-on experience with AWS ML services including SageMaker, EMR, Lambda, CloudWatch, and S3 Data Lake architecture (this is a must)

• Experience with ML model versioning, experiment tracking, and ML metadata management tools and Strong knowledge in ML-specific CI/CD pipelines using tools such as Jenkins, Spinnaker, Bitbucket, and MLflow (this is a must)

• Experience containerizing ML applications and deploying them on Kubernetes in AWS environment and Proficiency in data processing at scale using Spark, Dask, or similar distributed computing frameworks, in addition experience with ML monitoring and observability tools such as Prometheus, Grafana, or Cloud Watch.

Preferred qualifications, capabilities, and skills

• Computer science degree or equivalent experience

• AWS Certifications (AWS Solution architect, developer or ML Specialty)

• Agile fundamentals