Lead Software Engineer [Multiple Positions Available]

JPMorgan Chase & Co.•Tampa, FL

16h•Onsite

About The Position

The Lead Software Engineer will 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. This role involves building and optimizing applications for data ingestion, transformation, and retrieval, leveraging scalable storage and database platforms. The engineer will create automated extract, transform, and load (ETL) processes and optimize data pipelines for reliability and performance using orchestration and data engineering tools. Additionally, the position supports complex data analytics and reporting needs by integrating with enterprise data platforms and business intelligence tools. The role also includes implementing real-time and batch data processing solutions for analytics and machine learning using distributed computing frameworks like Apache, Spark, and Flink. A key aspect of this role is designing, training, and deploying machine learning (ML) and AI models to address business challenges, including predictive analytics and decision support. The engineer will develop and maintain models and systems for fraud detection and risk mitigation, ensuring alignment with industry best practices and regulatory requirements. The position requires using infrastructure automation tools to provision, manage, and scale cloud and on-premises resources efficiently. Building and deploying serverless solutions to enable flexible, scalable, and cost-effective application architectures is also a responsibility. Furthermore, the role involves implementing strategies to monitor, manage, and reduce cloud infrastructure expenses while maintaining performance and reliability, and maintaining compliance with relevant regulations and standards, implementing 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 using distributed computing frameworks.
Design, train, and deploy machine learning (ML) and AI models to address business challenges.
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.