Rohit Gujral

• • Achieved significant cost savings by implementing workload downscaling in lower environments, using automated downscaler solutions to reduce resource consumption during non-working hours.

• • Further optimised operational costs by deploying time-based scaling with KEDA, dynamically adjusting workloads during periods of low traffic.

• • Enhanced system observability and reliability by designing and deploying comprehensive Datadog monitors and custom dashboards, enabling proactive incident detection and faster resolution.

• • Implemented robust database replication using AWS DMS to support new product offerings, ensuring data consistency and high availability across platforms.

• • Improved processing efficiency and scalability by configuring Kafka topic consumers to auto-scale based on real-time demand using KEDA's Kafka scaler.

• • Led multiple DevOps initiatives, collaborating closely with development teams to streamline feature implementation and release cycles, accelerating time-to-market for new functionalities.

• • Configured Argocd for Continuous Deployment, streamlining release process and reducing deployment times by 20%

• • Built robust in-house monitoring solution using Prometheus, Thanos and Grafana improving system visibility and reducing response times to critical incidents by 10%

• • Developed and maintained various Terraform modules and configurations for infrastructure provisioning, reducing manual workload and increasing infrastructure-as-code efficiency

• • Implemented Sops as methodology for GitOps, enhancing security and compliance in code deployments.

• • Designed and administered CI/CD infrastructure using bitbucket pipelines to build, test and deploy software which increased our software delivery process.

• • Strategically reduced infrastructure costs by 20% by optimising resource allocation.

• • Configured traffic management & service mesh software using istio which enhanced cluster security.

• • Authored various Bitbucket pipelines and used Bash and Python scripting for automation, significantly reducing manual tasks.

• • Performance improvement for python andjava based microservices.

• • Implemented synk, AWS WAF as security measure while working closely with security team.

• • CI/CD Setup for Product Deployment: Reduced deployment time by 30% through implementation of end-to end CI/CD pipeline. Achieved 20% increase in deployment frequency, enabling faster and more efficient releases.

• • Migration to Amazon EKS: Improved infrastructure performance, resulting in 25% reduction in response time. Increased scalability by 40% to accommodate growing workloads.

• • Integration of Various Technologies: Strengthened system monitoring and security using prometheus stack, snyk etc, reducing incident response time by 20%.

• • MLOPS Adoption: Accelerated ML model development by 15% through adoption of MLOPS practices. Enabled ML team to release models 15% faster with Kubeflow.

• • Collaboration with Data Engineering & Data Scientist Team: Delivered new cloud capabilities for ML, AI, Data, and Analytics, resulting in 20% improvement in data processing efficiency.

• • Implementation of Druid and Dagster: Improved data analytics capabilities with Druid, resulting in 25% reduction in query response time. Enhanced reliability of machine learning data pipelines, reducing data pipeline failures by 15%.

• • Establishment of Release Management Processes: Ensured organized software release cycles, reducing post-release defects by 20%. Achieved 95% success rate in first-time-right releases through efficient branching strategies.