The Software Environment Developer will design and manage build environments, optimize CI/CD pipelines, and integrate AI tools to enhance developer productivity while collaborating in a multi-geography team.
Join our Team
About the Opportunity
You will be part of Software Environment Developer (SED) team whose primary purpose is to secure a stable and efficient build environment aimed for components and products.
In this role you will also work with AI-powered tools and services to enhance developer productivity, build and test reliability, and observability. You will help integrate and operationalize AI capabilities in our CI/CD and developer environments, ensuring they are robust, secure, and aligned with Ericsson ways of working.
What you will do:
Define architecture, develop, maintain, and administer the product build environment to meet business needs, adhering to relevant build frameworks and recommendations.
Plan and analyze changes in the build environment to ensure alignment with business objectives.
Troubleshoot and optimize CI/CD pipelines, build infrastructure, and test environments.
Work collaboratively in a hybrid set-up, engaging with a multi-geography team to achieve shared goals
What you must have:
B.E /B Tech /M Tech preferably in Computer Science or Electronics or Masters in Computer Application (MCA)
5 - 6 yrs of experience, proficiency in Linux administration and troubleshooting.
Strong experience in shell scripting for automation and system tasks.
Advanced knowledge of Python scripting for automation and tooling.
Hands-on experience with Jenkins for continuous integration and continuous deployment (CI/CD) pipelines.
Advanced knowledge of working with Git/Gerrit for version control and code review processes.
Fundamental understanding of Docker for containerization.
Fundamental knowledge of Kubernetes for container orchestration.
Basic IP network skills for troubleshooting and configuration.
AI & automation skills (working with AI)
Hands-on experience using AI-assisted developer tools (e.g., LLM-based code assistants, AI-based log analysis, AI test generation) as part of daily engineering work.
Ability to formulate effective prompts and problem descriptions for AI tools to troubleshoot complex build, test, or environment issues.
Experience integrating SaaS/on-prem AI services into engineering workflows or CI/CD pipelines (for example, via REST APIs, webhooks, or plug-ins for Jenkins/Git).
Awareness of AI governance, security, and data-handling considerations (e.g., what can/cannot be sent to external AI services; handling of logs and code securely).
Added Advantages to have:
Prior experience in maintaining and troubleshooting software delivery staging & release pipelines in a cloud-native development environment.
Prior experience of working in a DevOps / platform engineering setup.
Exposure to MLOps or AI platform tooling (e.g., model deployment/monitoring platforms, feature stores) or working closely with data/ML teams.
Experience building chatbot- or assistant-like solutions for internal developer support (for example, integrating AI Q&A over internal documentation).
Experience with observability stacks (Prometheus, Grafana, ELK, etc.) and using AI/ML on top of telemetry data for anomaly detection or predictive insights.
About the Opportunity
You will be part of Software Environment Developer (SED) team whose primary purpose is to secure a stable and efficient build environment aimed for components and products.
In this role you will also work with AI-powered tools and services to enhance developer productivity, build and test reliability, and observability. You will help integrate and operationalize AI capabilities in our CI/CD and developer environments, ensuring they are robust, secure, and aligned with Ericsson ways of working.
What you will do:
Define architecture, develop, maintain, and administer the product build environment to meet business needs, adhering to relevant build frameworks and recommendations.
Plan and analyze changes in the build environment to ensure alignment with business objectives.
Troubleshoot and optimize CI/CD pipelines, build infrastructure, and test environments.
Work collaboratively in a hybrid set-up, engaging with a multi-geography team to achieve shared goals
What you must have:
B.E /B Tech /M Tech preferably in Computer Science or Electronics or Masters in Computer Application (MCA)
5 - 6 yrs of experience, proficiency in Linux administration and troubleshooting.
Strong experience in shell scripting for automation and system tasks.
Advanced knowledge of Python scripting for automation and tooling.
Hands-on experience with Jenkins for continuous integration and continuous deployment (CI/CD) pipelines.
Advanced knowledge of working with Git/Gerrit for version control and code review processes.
Fundamental understanding of Docker for containerization.
Fundamental knowledge of Kubernetes for container orchestration.
Basic IP network skills for troubleshooting and configuration.
AI & automation skills (working with AI)
Hands-on experience using AI-assisted developer tools (e.g., LLM-based code assistants, AI-based log analysis, AI test generation) as part of daily engineering work.
Ability to formulate effective prompts and problem descriptions for AI tools to troubleshoot complex build, test, or environment issues.
Experience integrating SaaS/on-prem AI services into engineering workflows or CI/CD pipelines (for example, via REST APIs, webhooks, or plug-ins for Jenkins/Git).
Awareness of AI governance, security, and data-handling considerations (e.g., what can/cannot be sent to external AI services; handling of logs and code securely).
Added Advantages to have:
Prior experience in maintaining and troubleshooting software delivery staging & release pipelines in a cloud-native development environment.
Prior experience of working in a DevOps / platform engineering setup.
Exposure to MLOps or AI platform tooling (e.g., model deployment/monitoring platforms, feature stores) or working closely with data/ML teams.
Experience building chatbot- or assistant-like solutions for internal developer support (for example, integrating AI Q&A over internal documentation).
Experience with observability stacks (Prometheus, Grafana, ELK, etc.) and using AI/ML on top of telemetry data for anomaly detection or predictive insights.
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