You re ready to deepen your skills, make an impact, and accelerate your career and we have the ideal software engineering opportunity for you.
As a Lead Software Engineer at JPMorgan Chase within the Consumer Community Banking organization, you re ready to deepen your skills, make an impact, and accelerate your career and we have the ideal software engineering opportunity for you. This role is pivotal in modernizing our technology stack and enabling scalable, resilient, and efficient operations.
Job Responsibilities:
- Design and implement CI/CD pipelines, infrastructure-as-code (IaC) frameworks, and container orchestration strategies leveraging tools such as Kubernetes, Docker, Terraform, and Spinnaker, while utilizing AI-driven automation to streamline deployment and management across cloud and on-premises environments.
- Lead the architecture, deployment, and management of cloud infrastructure in AWS, establishing and enforcing best practices for reliability, scalability, security, and cost optimization across all cloud environments.
-
Drive the adoption of AI and machine learning capabilities within DevOps workflows, including intelligent monitoring, predictive analytics, and automated remediation, while evaluating and integrating AI-powered tools to continuously improve development velocity, system reliability, and operational efficiency.
-
Lead the integration of intelligent agents for workflow automation, decision-making, and process optimization.
-
Develop AI-powered observability solutions to monitor, analyze, and proactively manage application and infrastructure health, automating alerting, root cause analysis, and incident response using advanced ML techniques.
-
Stay abreast of emerging AI/ML technologies, frameworks, and industry trends, driving continuous improvement by evaluating and implementing new tools, methodologies, and approaches.
-
Provide hands-on technical guidance to a team of software and DevOps engineers, fostering a culture of innovation, accountability, and continuous learning.
-
Conduct code reviews, architectural assessments, and design discussions to uphold engineering excellence.
-
Act as a key liaison between engineering, product, and leadership teams to align technical strategy with business objectives.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Strong experience in AI/ML engineering, with proven expertise in agent-based systems and automation.
- Strong experience in automating IAC development (e.g., Terraform, Ansible, CloudFormation) using AI/ML.
- Deep understanding of observability tools (e.g., Prometheus, Grafana, ELK stack) and automation using AI/ML.
- Proficiency in Python, Java, or similar programming languages; experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn).
- Familiarity with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
- Excellent problem-solving, communication, and collaboration skills.
Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.
5-10 years96
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Job Description
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You re ready to deepen your skills, make an impact, and accelerate your career and we have the ideal software engineering opportunity for you.
As a Lead Software Engineer at JPMorgan Chase within the Consumer Community Banking organization, you re ready to deepen your skills, make an impact, and accelerate your career and we have the ideal software engineering opportunity for you. This role is pivotal in modernizing our technology stack and enabling scalable, resilient, and efficient operations.
Job Responsibilities:
- Design and implement CI/CD pipelines, infrastructure-as-code (IaC) frameworks, and container orchestration strategies leveraging tools such as Kubernetes, Docker, Terraform, and Spinnaker, while utilizing AI-driven automation to streamline deployment and management across cloud and on-premises environments.
- Lead the architecture, deployment, and management of cloud infrastructure in AWS, establishing and enforcing best practices for reliability, scalability, security, and cost optimization across all cloud environments.
-
Drive the adoption of AI and machine learning capabilities within DevOps workflows, including intelligent monitoring, predictive analytics, and automated remediation, while evaluating and integrating AI-powered tools to continuously improve development velocity, system reliability, and operational efficiency.
-
Lead the integration of intelligent agents for workflow automation, decision-making, and process optimization.
-
Develop AI-powered observability solutions to monitor, analyze, and proactively manage application and infrastructure health, automating alerting, root cause analysis, and incident response using advanced ML techniques.
-
Stay abreast of emerging AI/ML technologies, frameworks, and industry trends, driving continuous improvement by evaluating and implementing new tools, methodologies, and approaches.
-
Provide hands-on technical guidance to a team of software and DevOps engineers, fostering a culture of innovation, accountability, and continuous learning.
-
Conduct code reviews, architectural assessments, and design discussions to uphold engineering excellence.
-
Act as a key liaison between engineering, product, and leadership teams to align technical strategy with business objectives.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Strong experience in AI/ML engineering, with proven expertise in agent-based systems and automation.
- Strong experience in automating IAC development (e.g., Terraform, Ansible, CloudFormation) using AI/ML.
- Deep understanding of observability tools (e.g., Prometheus, Grafana, ELK stack) and automation using AI/ML.
- Proficiency in Python, Java, or similar programming languages; experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn).
- Familiarity with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
- Excellent problem-solving, communication, and collaboration skills.
Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.