Lead Software Engineer [Multiple Positions Available]

JPMorganChase•Tampa, FL

7h•Onsite

About The Position

Develop and maintain data storage and processing solutions to support efficient data management and analytics, utilizing modern cloud and on-premises technologies such as data lakes and distributed databases. Build and optimize applications for data ingestion, transformation, and retrieval, leveraging scalable storage and database platforms. Create automated extract, transform, and load (ETL) processes and optimize data pipelines for reliability and performance using orchestration and data engineering tools. Support complex data analytics and reporting needs by integrating with enterprise data platforms and business intelligence tools. Implement real-time and batch data processing solutions for analytics and machine learning using distributed computing frameworks including Apache, Spark, and Flink. Design, train, and deploy machine learning (ML) and AI models to address business challenges, including predictive analytics and decision support. Develop and maintain models and systems for fraud detection and risk mitigation, ensuring alignment with industry best practices and regulatory requirements. Use infrastructure automation tools to provision, manage, and scale cloud and on-premises resources efficiently. Build and deploy serverless solutions to enable flexible, scalable, and cost-effective application architectures. Implement strategies to monitor, manage, and reduce cloud infrastructure expenses while maintaining performance and reliability. Maintain compliance with relevant regulations and standards, and implement robust data security and governance practices.

Requirements

Bachelor's degree in Electrical Engineering, Electronic Engineering, Computer Engineering, Computer Information Systems, or related field of study
7 years of experience in the job offered or as Lead Software Engineer, Software Engineer/Developer, Programmer Analyst, Technical Lead, or related occupation
Designing, implementing, and optimizing machine learning algorithms and AI solutions using Python
Data preprocessing, feature engineering, model selection, and hyperparameter tuning
Developing end-to-end ML pipelines, from data ingestion to model deployment and monitoring
Architecting and deploying scalable data processing workflows using AWS Glue for ETL, AWS Athena, Step Functions for orchestration, S3, and elastic map reduce for distributed data analytics
Designing cloud-native solutions leveraging AWS services with security, cost optimization, and high availability
Utilizing cloud data warehousing features to enable complex queries, data modeling, and business intelligence
Applying best practices for data validation, cleansing, and governance within cloud data environments
Developing and maintaining data models, data lakes, and data warehouses
Implementing analytics solutions and optimizing query performance and data flow
Authoring and maintaining Terraform scripts to automate cloud infrastructure provisioning and management, repeatability, and compliance
Developing and deploying serverless applications and integrating them with AWS Lambda for event-driven processing
Building and enhancing fraud detection models, including feature engineering, entity resolution, and real-time scoring by using domain-specific techniques
Identifying fraud patterns and translating business requirements into effective ML solutions
Developing, validating, and deploying supervised, unsupervised, and reinforcement learning models
Evaluating model performance using metrics and implementing strategies for continuous improvement
Applying deep learning techniques using TensorFlow, PyTorch, Keras, or Scikit-learn for model development, training, and evaluation

Responsibilities

Develop and maintain data storage and processing solutions utilizing modern cloud and on-premises technologies.
Build and optimize applications for data ingestion, transformation, and retrieval.
Create automated extract, transform, and load (ETL) processes and optimize data pipelines.
Support complex data analytics and reporting needs by integrating with enterprise data platforms and business intelligence tools.
Implement real-time and batch data processing solutions for analytics and machine learning.
Design, train, and deploy machine learning (ML) and AI models.
Develop and maintain models and systems for fraud detection and risk mitigation.
Use infrastructure automation tools to provision, manage, and scale cloud and on-premises resources.
Build and deploy serverless solutions.
Implement strategies to monitor, manage, and reduce cloud infrastructure expenses.
Maintain compliance with relevant regulations and standards, and implement robust data security and governance practices.