Sanskar Goyal

Dynamic Cloud Engineer with experience at leading technology firms, driving impactful solutions in cloud computing. Proficient in leveraging Amazon AWS and container orchestration with Docker and Kubernetes to enhance operational efficiency. Expertise in automation and infrastructure as code through Terraform and CI/CD practices, ensuring seamless deployment and scalability. Committed to continuous learning and adapting to emerging technologies to deliver innovative cloud solutions that meet business needs. Seeking to contribute technical acumen and problem-solving skills in a challenging role

• HashiCorp Certified: Terraform Associate (003)

https://www.credly.com/badges/ddbc38cb-388b-40b3-810d-12de9fe156e6/linked_in_profile

Incident Reassignment and Remediation

• Automated Incident Reassignment and Remediation:
• Developed and implemented automated workflows for incident reassignment, ensuring timely escalation and resolution based on predefined SLAs, reducing manual intervention by 50%.
• Utilized ServiceNow integration to automate the assignment of incidents to the appropriate teams, improving response time by 80%.
• Automated remediation actions using Python, enabling rapid resolution of recurring incidents and reducing incident recurrence by 40%.
• Incident Resolution Process Optimization:
• Designed a Python-based automation tool for real-time monitoring of incident queues, triggering auto-remediation scripts to resolve incidents autonomously based on root cause analysis.
• Reduced the average time to resolve high-priority incidents by automating manual incident investigation and remediation processes.
• Cross-Functional Automation Solutions:
• Led the development of an automated incident remediation system in collaboration with the IT security and operations teams, reducing the need for manual intervention and improving system uptime by 25%.
• Integrated incident management tools with third-party systems like Pagerduty and Datadog to trigger automatic alerts, reassignment, and remediation actions for critical incidents.

Sentimental Analysis

Developed a sentiment analysis model using Python 3, OpenCV, CNN, RNN, and TensorFlow to analyze both customer reviews (text) and customer service call transcripts (speech). Employed OpenCV for preprocessing visual data (if applicable), used speech-to-text conversion for analyzing audio, and implemented CNN for feature extraction and RNN (LSTM) for sentiment classification. Achieved 85% classification accuracy, providing actionable insights that increased customer engagement by 15%.

• Music
• Cricket and Football(buff)
• Movies