About Salman Malik:
🔹 As a seasoned DevOps Engineer, I bring a unique blend of skills in CI/CD pipeline design, containerisation, orchestration, and cloud technologies. I have a proven track record of leveraging tools like Docker, Kubernetes, Ansible, and Terraform to automate and streamline operations, ensuring efficient and reliable deployment processes.
🔹 My work at Rapid Solutions has allowed me to demonstrate my ability to implement robust and scalable cloud-based solutions using AWS technologies. I've leveraged EC2 instances, RDS databases, and AWS Lambda for serverless computing, managing complex data pipelines and optimising data schemas. Engineered an automated system leveraging AWS services including Lambda, S3, SNS, and IAM, using Python and Boto3.
🔹 I have a strong foundation in mathematics, statistics, and computer science, which has been instrumental in my data-driven approach to problem-solving. This has been particularly useful in my work with machine learning techniques, where I've applied my skills in data analysis, visualisation, and big data automation to real-world challenges.
🔹 My technical skills extend to Python, Helm and Kustomize, GoLang, Docker/Rancher, Kubernetes and Operators, Ansible, Terraform, and CI/CD. I am proficient in shell scripting, which I've used extensively to automate tasks and manage deployments.
🔹 My GitHub repositories reflect my hands-on experience with various projects. For instance, I've developed an end-to-end CI/CD pipeline for a Java application using Jenkins, SonarQube, Argo CD, and Kubernetes. This project showcased my ability to automate the entire process from code checkout, build, test, code quality analysis, to deployment.
🔹 One of my notable projects involved developing a machine learning model by implementing the best practices of MLOps using Streamlit for predicting Airbnb listing prices. I used Docker for containerisation and Kubernetes for orchestration. This project involved creating a network between two Docker containers and automating the process of building and deploying Docker images through GitHub Actions, showcasing my expertise in automating and streamlining operations.
🔹 I am always seeking opportunities to grow and learn. My unique blend of DevOps and data science skills enables me to deliver comprehensive solutions that drive efficiency and innovation. I am passionate about developing customised solutions and delivering tangible value to clients.
• One of my major accomplishments was designing and implementing the Databricks Data Lakehouse platform.
• In-house Development environment using Docker and Kubernetes. Using upstream operators like Cloud Native Postgres, Elasticsearch and MariaDB.
• Kubernetes and Monitoring metrics using the official helm chart Kube Prometheus Stack.
• Central logging using Loki.
• Publishing docker images using Artifactory.
• AWS for cloud-based solutions, leveraging EC2 instances, RDS databases, S3,
Lambda and CloudWatch to store, process, monitor and manage client data.
• Team collaboration and continuous improvement in infrastructure development by
integrating Terraform code with Git.
• Implemented GitOps best practices and introduced Ansible for configuration
management, enhancing deployment traceability, enabling automated rollbacks, and
automating server setup and maintenance.
• Utilised vSphere for virtualisation, enhancing our infrastructure's efficiency and
• GitLab CI/CD pipelines assisted to automate the building and deployment of data
pipelines for ETL processes.
• Gained proficiency in Linux system administration, ensuring the smooth operation of
our development environment.
• Worked on the Hadoop ecosystem, including HDFS and Hive to store and process
large volumes of structured and unstructured data.
• Used Jira for collaboration and project management to track project progress and
ensure timely delivery.• One of
Master Data Science(Ms)
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