JPMorgan Data Engineer III - Python/AIML 

United States, New Jersey, Jersey City 

100969544

Job responsibilities

• Design and develop data pipelines to preprocess and transform data for AI/ML solutions.
• Collaborate with cross-functional teams to understand design requirements and translate them into technical solutions.
• Effectively understand and navigate a complex, enterprise technical stack.
• Monitor and maintain deployed models, ensuring their performance and reliability.
• Design and work with cloud-based architectures.
• Support review of controls to ensure sufficient protection of enterprise data.
• Advise and make custom configuration changes in one to two tools to generate a product at the business or customer request.
• Update logical or physical data models based on new use cases.
• Frequently use SQL and understand NoSQL databases and their niche in the marketplace.
• Add to team culture of diversity, equity, inclusion, and respect.

Required qualifications, capabilities, and skills

• Formal training or certification on Data Engineering concepts and 3+ years applied experience.
• Experience in software development, including software development life cycle, coding standards, documentation, code reviews, source control management, continuous integration, build processes, testing, and operations experience.
• Demonstrate proficiency in Python, with experience in developing and maintaining production-level code.
• Exhibit proficiency in data engineering, querying various types of data stores, efficiently moving data, working with large datasets, and data preprocessing.
• Have experience with cloud platforms, such as AWS and Azure, for designing and deploying infrastructure to run processes, web apps, and training and inference pipelines for AI/ML models, including handling networking and scaling of these systems.
• Show experience with workflow management and orchestration tools such as Airflow and Kubernetes.
• Possess strong problem-solving and analytical skills.
• Demonstrate excellent documentation, communication, and collaboration skills.
• Have knowledge of infrastructure operations.
• Experience across the data lifecycle.
• Possess significant experience with statistical data analysis and ability to determine appropriate tools and data patterns to perform analysis.

• Have experience in the design or architecture of new and existing systems, including design patterns, reliability, and scaling.
• Demonstrate experience in machine learning engineering.
• Be familiar with DevOps practices for AI/ML model deployment and monitoring.
• Have knowledge of graph databases and familiarity with graph query languages.