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Job Overview
A Platform Engineer is responsible for developing, maintaining, and optimizing the tools and systems that support the software development lifecycle. This role focuses on creating efficient, scalable, and reliable platforms that enhance the productivity of development teams and ensure seamless integration and deployment of applications.
Responsibilities
- Tool Development: Design, develop, and maintain tools that support software development, testing, and deployment processes.
- Automation: Implement automation solutions to streamline workflows and reduce manual intervention.
- Integration: Ensure seamless integration of tools with existing systems and platforms.
- Monitoring and Maintenance: Monitor the performance of tools and systems, troubleshoot issues, and perform regular maintenance to ensure reliability.
- Collaboration: Work closely with development, operations, and quality assurance teams to understand their needs and provide effective solutions.
- Documentation: Create and maintain documentation for tools and processes to ensure clear communication and knowledge sharing.
- Innovation: Stay updated with the latest technologies and best practices in platform engineering and tool development.
- Security: Implement security measures to protect tools and systems from potential threats.
- Training and Support: Provide training and support to team members on the use of tools and systems.
Technical Skills
- Programming Languages: Proficiency in languages like Python, Java, Groovy, or Go.
- Operating Systems: Proficiency in Linux(RHEL) and Windows systems.
- SDLC Tools: Experience with tools such as Jira, Confluence, Bitbucket, Nexus, Zephyr.
- CI/CD Tools: Experience with tools such as Jenkins, Bamboo, GitLab, Ansible.
- Cloud Platforms: Knowledge of cloud services like AWS, Azure, or Google Cloud Platform.
- Security: Experience with tools for delivering SCA, SAST, DAST capabilities.
- Monitoring and Logging: Proficiency with tools like Splunk, Dynatrace, Datadog, Prometheus, Grafana.
- Version Control: Strong understanding of Git and version control practices.
- Scripting: Skills in scripting languages like Bash, PowerShell, or Perl.
- Containerization: Familiarity with Docker and orchestration tools like Kubernetes.
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AI Skills
- AI-Assisted Coding and Scripting: Use AI coding assistants to accelerate Python, Bash, PowerShell, Groovy, and pipeline script development while validating generated code for correctness, security, and maintainability.
- Prompt Engineering for Engineering Workflows: Create clear prompts for troubleshooting, documentation, and operational runbooks to improve speed and consistency.
- AIOps and Intelligent Observability: Apply AI to analyze logs, metrics, traces, alerts, and incident history to detect anomalies, identify root causes, reduce noise, and improve mean time to resolution.
- AI-Enhanced CI/CD Optimization: Use AI to identify flaky tests, optimize build and deployment pipelines, review pipeline configuration, estimate deployment risk, and recommend rollback or release strategies.
- Infrastructure as Code Generation and Review: Leverage AI to draft, refactor, and review Terraform, Ansible, Kubernetes, and cloud configuration templates while enforcing standards and guardrails.
- AI-Driven Security and Compliance: Use AI-assisted scanning and analysis to support SCA, SAST, DAST, secrets detection, cloud misconfiguration checks, and remediation planning.
- Knowledge Management and Documentation Automation: Use AI to summarize technical discussions, generate onboarding guides, maintain tool documentation, and convert operational knowledge into reusable self-service content.
- AI Workflow Automation: Design AI-assisted automations and lightweight agents that reduce repetitive manual tasks across ticket triage, environment provisioning, incident response, and developer support.
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Soft Skills
- Collaboration: Excellent teamwork and communication skills to work effectively with cross-functional teams.
- Problem-Solving: Strong analytical and troubleshooting abilities to resolve issues quickly.
- Adaptability: Ability to adapt to new technologies and methodologies in a fast-paced environment.
- Attention to Detail: Precision in managing configurations and deployments to avoid errors.
- Continuous Learning: Commitment to staying updated with the latest trends and tools in platform engineering.
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Skills
Python
Java
Groovy
Go
Linux
Windows
Jira
Confluence
Bitbucket
Jenkins
AWS
Docker
Kubernetes
Git
AI
Automation
Location