
at J.P. Morgan
Bulge Bracket Investment BanksPosted 3 days ago
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**Site Reliability Engineer III** drives innovation by solving complex business problems through cloud infrastructure and code. In Bengaluru, this mid-level role requires 3+ years in software engineering and proficiency in Python, Java/Spring Boot, or .Net. Collaborate with teams to design deployments and implement CI/CD pipelines, using tools like Jenkins, GitLab, or Terraform. Ensure application reliability, monitoring observability with Grafana, Dynatrace, or Prometheus. Contribute to teams, presenting information logically and unsupervised, while validating AI-assisted recommendations. Familiarity with Linux, Kubernetes, and Docker is beneficial. Share knowledge to maintain and optimize applications, driving SRE best practices.
- Compensation
- Not specified
- City
- Bengaluru
- Country
- India
Currency: Not specified
Full Job Description
Location: Bengaluru, Karnataka, India
Theres nothing more exciting than being at the center of a rapidly growing field in technology and applying your skillsets to drive innovation and modernize the world's most complex and mission-critical systems.
As a Site Reliability Engineer III at JPMorgan Chase within the Commercial & Investment Bank, you will solve complex and broad business problems with simple and straightforward solutions. Through code and cloud infrastructure, you will configure, maintain, monitor, and optimize applications and their associated infrastructure to independently decompose and iteratively improve on existing solutions. You are a significant contributor to your team by sharing your knowledge of end-to-end operations, availability, reliability, and scalability of your application or platform.
Job responsibilities
- Guides and assists others in the areas of building appropriate level designs and gaining consensus from peers where appropriate
- Collaborates with other software engineers and teams to design and implement deployment approaches using automated continuous integration and continuous delivery pipelines
- Uses enterprise-authorized AI capabilities within the work environment to accelerate incident triage, troubleshooting, and post-incident analysis, validating outputs and handling operational data according to sensitivity and security requirements.
- Collaborates with other software engineers and teams to design, develop, test, and implement availability, reliability, scalability, and solutions in their applications
- Applies enterprise-authorized AI capabilities within the work environment to identify patterns in operational signals that indicate reliability risk or recurring toil, prioritizing reuse-first improvements tied to SLO outcomes.
- Implements infrastructure, configuration, and network as code for the applications and platforms in your remit
- Collaborates with technical experts, key stakeholders, and team members to resolve complex problems
- Understands service level indicators and utilizes service level objectives to proactively resolve issues before they impact customers
- Supports the adoption of site reliability engineering best practices within your team
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 3+ years applied experience
- Proficient in site reliability culture and principles and familiarity with how to implement site reliability within an application or platform
- Proficient in at least one programming language such as Python, Java/Spring Boot, and .Net
- Proficient knowledge of software applications and technical processes within a given technical discipline (e.g., Cloud, artificial intelligence, Android, etc.)
- Experience in observability such as white and black box monitoring, service level objective alerting, and telemetry collection using tools such as Grafana, Dynatrace, Prometheus, Datadog, Splunk, and others
- Experience with continuous integration and continuous delivery tools like Jenkins, GitLab, or Terraform
- Familiarity with container and container orchestration such as ECS, Kubernetes, and Docker
- Working knowledge of using enterprise-authorized AI capabilities within the work environment to support SRE workflows with strong validation habits and awareness of data sensitivity
- Ability to validate AI-assisted operational recommendations before applying changes, escalating when uncertain and following data sensitivity requirements
- Familiarity with Linux OS and kernel concepts and good to have prior experience of using linux commands to debug and profile applications
- Ability to contribute to large and collaborative teams by presenting information in a logical and timely manner with compelling language and limited supervision




