J.P. Morgan
We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.
As a Software Engineer III at JPMorgan Chase within the Corporate Technology organization, you will serve as a seasoned member of an agile team, designing and delivering trusted, market-leading technology products in a secure, stable, and scalable manner. You will be responsible for implementing critical technology solutions across multiple technical areas within various business functions, supporting the firm’s business objectives.
Job responsibilities
Designs, develops, deploys, and manages AI/ML models using advanced techniques, LLMs, and Agentic AI for compliance and risk management solutions. Conducts research on AI techniques to enhance model performance Designs, builds, tests, and deploys automation scripts, monitoring tools, and machine learning models to support IT operations; ensures solutions are scalable, maintainable, and aligned with business requirements Implements and refines algorithms for anomaly detection, predictive incident analysis, and automated remediation while integrating these solutions with existing monitoring and alerting platforms to reduce manual intervention and improve response times Integrates AIOps solutions with IT service management (ITSM) tools, CI/CD pipelines, and cloud infrastructure; automates repetitive operational tasks such as health checks, resource provisioning, and incident triage Collect, clean, and process operational data from diverse sources such as logs, metrics, events, and traces Works closely with SREs, infrastructure, application, and support teams to understand operational pain points and requirements Participates in code reviews, contributes to best practices, and help optimize existing AIOps workflows; monitors solution performance and iterate based on feedback and operational metrics Ensures all automation and data handling practices comply with organizational security policies and regulatory requirements; collaborates with security teams to address vulnerabilities and maintain data integrity Develops and maintains tools and frameworks for model training, evaluation, and optimization Creates and maintains clear documentation for developed solutions, processes, and troubleshooting guides and trains team members and stakeholders on AIOps tools and practices Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 3+ years applied experience Hands-on practical experience in system design, application development, testing, and operational stability Proficient hands-on coding experience in
Java and Python , with experience in leveraging available libraries, such as Tensorflow, Keras, Pytorch, or Scikit-learn to dedicated projects Experience in Artificial Intelligence (AI) Model development such as Generative AI (GenAI) and large language models (LLM) Demonstrated knowledge of software applications and technical processes within a technical discipline Artificial Intelligence (AI) and Machine Learning (ML) Strong experience in natural language processing (NLP) and its applications Proficient knowledge of cloud technologies such as
Azure Overall knowledge of the Software Development Life Cycle and solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security Demonstrated experience in working on Spark, Hive, and SQL Preferred qualifications, capabilities, and skills
Familiarity with modern front-end technologies Proficient Knowledge of AWS cloud and its basic services Familiarity with MLOps tools and practices for seamless integration of machine learning models into production
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Designs, develops, deploys, and manages AI/ML models using advanced techniques, LLMs, and Agentic AI for compliance and risk management solutions. Conducts research on AI techniques to enhance model performance Designs, builds, tests, and deploys automation scripts, monitoring tools, and machine learning models to support IT operations; ensures solutions are scalable, maintainable, and aligned with business requirements Implements and refines algorithms for anomaly detection, predictive incident analysis, and automated remediation while integrating these solutions with existing monitoring and alerting platforms to reduce manual intervention and improve response times Integrates AIOps solutions with IT service management (ITSM) tools, CI/CD pipelines, and cloud infrastructure; automates repetitive operational tasks such as health checks, resource provisioning, and incident triage Collect, clean, and process operational data from diverse sources such as logs, metrics, events, and traces Works closely with SREs, infrastructure, application, and support teams to understand operational pain points and requirements Participates in code reviews, contributes to best practices, and help optimize existing AIOps workflows; monitors solution performance and iterate based on feedback and operational metrics Ensures all automation and data handling practices comply with organizational security policies and regulatory requirements; collaborates with security teams to address vulnerabilities and maintain data integrity Develops and maintains tools and frameworks for model training, evaluation, and optimization Creates and maintains clear documentation for developed solutions, processes, and troubleshooting guides and trains team members and stakeholders on AIOps tools and practices Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 3+ years applied experience Hands-on practical experience in system design, application development, testing, and operational stability Proficient hands-on coding experience in
Java and Python , with experience in leveraging available libraries, such as Tensorflow, Keras, Pytorch, or Scikit-learn to dedicated projects Experience in Artificial Intelligence (AI) Model development such as Generative AI (GenAI) and large language models (LLM) Demonstrated knowledge of software applications and technical processes within a technical discipline Artificial Intelligence (AI) and Machine Learning (ML) Strong experience in natural language processing (NLP) and its applications Proficient knowledge of cloud technologies such as
Azure Overall knowledge of the Software Development Life Cycle and solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security Demonstrated experience in working on Spark, Hive, and SQL Preferred qualifications, capabilities, and skills
Familiarity with modern front-end technologies Proficient Knowledge of AWS cloud and its basic services Familiarity with MLOps tools and practices for seamless integration of machine learning models into production
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