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Job Description & How to Apply Below
Join our A player Dev Ops team, leading our client production and development infrastructure end to end.
Reporting to: Dev Ops TL
Location :
Gurugram, India (Hybrid)
Direct employment
Responsibilities:
Promote agile development processes in client's leading network security suite of products
- Design, implement and maintain CI/CD solutions running on AWS using Jenkins.
Manage the build environment (as part of the continuous integration solution), software and OS.
Manage multiple AWS accounts.
Collaborate with various teams to create Dev Ops solutions.
Troubleshoot and solve CI and production problems.
Position Requirements :
Requirements:
BSc in Computer Science/Engineering or equivalent.
At least 5 years of experience in Dev Ops roles.
Experience with infrastructure as code (IaC) - Terraform.
Experience in Establishing CI/CD procedures.
Working experience with Linux.
Experience in Bash, Python / Java, Groovy scripting. – all must
Familiarity with Git source control managed by bitbucket or gitlab.
Deep understanding of cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).
Good understanding of Prompt Engineering principals
Proven ability to effectively use AI chatbot tools (e.g., ChatGPT) for tasks such as test scenario creation, troubleshooting, and workflow optimization
Experience working with AI-powered coding assistants (e.g., Git Hub Copilot, Cursor) within development environments
Proven ability to effectively use/develop AI Agents
Proven ability to effectively use/develop/configure MCPs
Proven ability to effectively use/develop skills
Proactive and open minded.
Team player, pleasant and with a high level of integrity.
Very organized, thorough, and devoted.
Bright, fast learner, independent.
Good written and verbal communication skills in English.
Advantages:
Experience with Java, Maven
Experience with ELK and Kafka
Experience with developing MCPs
Experience with transforming non-agentic system/SDLC into agentic system/ SDLC
Experience with RAG pipelines and semantic search (vector databases)
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