Muhammad is currently unavailable
Muhammad Anees Tahir
Verified Expert in Engineering
DevOps Engineer and Software Developer
Munich, Bavaria, Germany
Toptal member since October 14, 2022
Bio
Anees is a confident DevOps software engineer and certified AWS developer associate with over seven years of experience in software development. He is proficient in Google Cloud Platform (GCP), AWS, and Azure. He has deployed applications from various domains, such as data engineering, machine learning (ML), and recommendation engines. Anees has a proven ability to develop ETL applications on AWS and build CI/CD pipelines for ML platforms (including observability and scalability of systems).
Portfolio
Wemolo GmbH
AWS IoT, Serverless Framework, Architecture, AWS IAM, Amazon EKS, Kubernetes...
BT&M Investments LLC dba Qtego Fundraising Services
Amazon Web Services (AWS), Amazon EC2, Amazon RDS, AWS Elastic Beanstalk...
SimplyWise, Inc.
Kubernetes, Helm, Terraform, Amazon EKS, Django...
Experience
- DevOps - 5 years
- Containerization - 5 years
- Docker - 5 years
- Amazon Web Services (AWS) - 5 years
- Kubernetes - 4 years
- Elasticsearch - 4 years
- CircleCI - 4 years
- Google Cloud Platform (GCP) - 3 years
Preferred Environment
Amazon Web Services (AWS), Google Cloud Platform (GCP), Site Reliability Engineering (SRE)
The most amazing...
...application I've worked on as the sole DevOps engineer was Presize, which scaled to two million+ users in 2020.
Work Experience
Senior DevOps Engineer
2022 - PRESENT
Wemolo GmbH
- Saved up to $32,000 per year in AWS costs by auditing infrastructure, exploring 3rd-party tools.
- Introduced Kubernetes and deployed HiveMQ, enabling scalable messaging services and laying the groundwork for future microservices.
- Supported teams with infra maintenance, IAM, and Terraform modules, cutting deployment issues and onboarding time by around 30%.
- Led Metabase JAR deployments and rebuilt analytics infra post-incident, reducing downtime by 90% and restoring full observability across teams.
Technologies: AWS IoT, Serverless Framework, Architecture, AWS IAM, Amazon EKS, Kubernetes, GitLab, GitLab CI/CD, Elastic, Virtual Private Cloud (VPC), Amazon CloudWatch, Prometheus, AWS ECS Fargate
AWS Expert
2022 - 2023
BT&M Investments LLC dba Qtego Fundraising Services
- Configured and managed multiple Beanstalk environments to handle increased traffic and demand. Implemented auto-scaling policies to ensure optimal utilization of resources and cost-effectiveness.
- Implemented multi-region deployment strategies to ensure high availability and disaster recovery capabilities. Configured and maintained failover mechanisms to seamlessly switch to a secondary region in case of a failure.
- Established database replication strategies to ensure high availability and minimal downtime during maintenance and upgrades. Monitored and troubleshot database replication issues and made necessary adjustments to improve reliability.
- Integrated Datadog with the infrastructure to monitor and track the key performance metrics of the system. Analyzed the container metrics and identified performance bottlenecks.
- Collaborated with the development team to resolve technical challenges and improve the system's performance. Regularly monitored and analyzed the system's performance and made adjustments to ensure optimal operation.
- Tested and validated the disaster recovery plan regularly to ensure its effectiveness. Analyzed and evaluated the existing infrastructure for potential risks and failures.
Technologies: Amazon Web Services (AWS), Amazon EC2, Amazon RDS, AWS Elastic Beanstalk, Datadog, Cloud Services, Bash, Unix, Linux Administration, DevOps Engineer, Architecture, Containers, APIs, Load Testing, Firewalls, Shell Scripting, HAProxy, AWS ELB, Solution Architecture, CloudOps, AWS Command Line Interface (CLI), Monitoring
DevOps Engineer
2022 - 2022
SimplyWise, Inc.
- Fixed application performance monitoring issues with Datadog.
- Set up an NGINX Ingress controller deployment to handle around 25,000 requests a day.
- Developed back-end scaling for microservices deployed on Kubernetes.
Technologies: Kubernetes, Helm, Terraform, Amazon EKS, Django, Amazon Elastic Container Service (ECS), Datadog, Cloud Deployment, Flask, Continuous Delivery (CD), AWS Cloud Architecture, Cloud Architecture, Cloud Infrastructure, NGINX, Autoscaling, APM, DevOps Engineer, Architecture, ECS, Containers, APIs, Load Testing, Shell Scripting, HAProxy, AWS ELB, Solution Architecture, CloudOps, AWS Command Line Interface (CLI), Monitoring, Application Performance Monitoring
Founding Engineer
2020 - 2022
Presize GmbH
- Led the system and architecture design of core services for scale.
- Set up infrastructure automation using Terraform and scaled 30+ microservices using Kubernetes.
- Constructed seamless automated build scripts for CI/CD pipelines. Released management across all environments.
- Built an internal tool for billing based on ELK (Elastic Stack), Kafka, and PySpark. It was used by the sales team for $100,000 MRR bills and reduced billing efforts to 70%.
- Managed servers, applications, cloud services, and container orchestration engines. Saved $350,000 in cloud costs.
- Ensured high up-times for our system and low response times. Maintained 99.99% uptime as service-level agreements (SLA).
Technologies: Google Cloud Platform (GCP), Azure, Kubernetes, CircleCI, CI/CD Pipelines, Docker, Terraform, GitHub, Kubernetes HorizontalPodAutoscaler (HPA), GitHub Actions, Linux, Amazon Web Services (AWS), Containerization, Visual Studio Code (VS Code), Elasticsearch, DevOps, Python, Amazon Elastic Container Service (ECS), Container Orchestration, GitLab, Jenkins, Continuous Integration (CI), AWS Lambda, Amazon S3 (AWS S3), ELK (Elastic Stack), Azure DevOps, Infrastructure as Code (IaC), Bitbucket, Agile, Agile Workflow, SQL, PostgreSQL, Amazon EKS, Azure Kubernetes Service (AKS), Amazon CloudWatch, Amazon EC2, Amazon API Gateway, Datadog, Sentry, AWS CodeCommit, AWS CodeDeploy, Elastic APM, Jira, Amazon RDS, AWS IAM, MacOS, Safari, AWS DevOps, Git, Amazon Virtual Private Cloud (VPC), AWS CodePipeline, MySQL, Machine Learning, Helm, Site Reliability Engineering (SRE), Kibana, Logstash, PySpark, Python 3, Amazon Route 53, Load Balancers, Amazon Simple Notification Service (SNS), SSL, Django, Cloud Deployment, Flask, Redis, Continuous Delivery (CD), GitLab CI/CD, AWS Cloud Architecture, Cloud Architecture, Cloud Infrastructure, RabbitMQ, Caching, NGINX, AWS Auto Scaling, Amazon EC2 API, Autoscaling, Autoscaling Groups, APM, DevOps Engineer, Architecture, ECS, Containers, APIs, Load Testing, Firewalls, Shell Scripting, HAProxy, AWS ELB, IT Infrastructure, Solution Architecture, CloudOps, AWS Command Line Interface (CLI), Monitoring, AIOps
ProServe (Intern)
2019 - 2020
Amazon Web Services (AWS)
- Developed reusable technical artifacts to aid DevOps consultants.
- Deployed natural language processing (NLP) based search engine for better text-based searches.
- Set up scalable deployment of internal onboarding tool.
Technologies: DevOps, CI/CD Pipelines, Docker, GitHub, Linux, Amazon Web Services (AWS), Containerization, Visual Studio Code (VS Code), Python, Amazon Elastic Container Service (ECS), Container Orchestration, Continuous Integration (CI), AWS Lambda, Amazon S3 (AWS S3), Infrastructure as Code (IaC), Agile, Agile Workflow, SQL, Amazon EKS, Amazon CloudWatch, Amazon EC2, Amazon API Gateway, AWS CodeCommit, AWS CodeDeploy, Amazon RDS, AWS IAM, MacOS, Safari, AWS DevOps, Git, Amazon Virtual Private Cloud (VPC), AWS CodePipeline, Python 3, Amazon Route 53, Load Balancers, Amazon Simple Notification Service (SNS), SSL, Cloud Deployment, Redis, Continuous Delivery (CD), GitLab CI/CD, AWS Cloud Architecture, Cloud Architecture, Cloud Infrastructure, RabbitMQ, Caching, NGINX, AWS Auto Scaling, Amazon EC2 API, Autoscaling, Autoscaling Groups, APM, DevOps Engineer, Architecture, ECS, Containers, APIs, AWS ELB, IT Infrastructure, Solution Architecture, CloudOps, AWS Command Line Interface (CLI)
Interdisciplinary Project (TUM) (Intern)
2019 - 2019
Presize GmbH
- Developed cloud architecture and application design for deep learning-based solutions.
- Development of CI/CD pipelines for machine learning and web microservices.
- Leading Architecture review by GCP and AWS solution architects.
Technologies: CircleCI, Kubernetes, CI/CD Pipelines, Docker, Terraform, GitHub, Linux, Amazon Web Services (AWS), Containerization, Visual Studio Code (VS Code), DevOps, Container Orchestration, Continuous Integration (CI), AWS Lambda, Amazon S3 (AWS S3), ELK (Elastic Stack), Infrastructure as Code (IaC), Bitbucket, Agile, Agile Workflow, SQL, PostgreSQL, Amazon EKS, Amazon CloudWatch, Amazon EC2, Amazon API Gateway, Datadog, Sentry, AWS CodeCommit, AWS CodeDeploy, Elastic APM, Jira, Amazon RDS, AWS IAM, MacOS, Safari, AWS DevOps, Git, Amazon Virtual Private Cloud (VPC), AWS CodePipeline, MySQL, Site Reliability Engineering (SRE), Kibana, Python 3, Helm, Amazon Route 53, Load Balancers, Amazon Simple Notification Service (SNS), SSL, Cloud Deployment, Flask, Redis, Continuous Delivery (CD), GitLab CI/CD, AWS Cloud Architecture, Cloud Architecture, Cloud Infrastructure, RabbitMQ, Caching, NGINX, AWS Auto Scaling, Amazon EC2 API, Autoscaling, Autoscaling Groups, APM, DevOps Engineer, Architecture, Containers, APIs, Load Testing, Firewalls, Shell Scripting, HAProxy, AWS ELB, IT Infrastructure, Solution Architecture, CloudOps, AWS Command Line Interface (CLI)
Cloud Engineer
2017 - 2018
NorthBay Solutions
- Built a proof-of-concept for data-oriented enterprises.
- Collaborated with AWS solution architects and customers.
- Created and maintained architecture documents for big-data projects.
- Ran DevOps for big data, data lakes, IoT, and data ingestion projects.
Technologies: DevOps, AWS Lambda, CI/CD Pipelines, Docker, Terraform, GitHub, Linux, Big Data, Amazon Web Services (AWS), Containerization, Visual Studio Code (VS Code), Elasticsearch, Amazon Elastic Container Service (ECS), Container Orchestration, GitLab, Jenkins, Continuous Integration (CI), Amazon S3 (AWS S3), ELK (Elastic Stack), Infrastructure as Code (IaC), Bitbucket, Jenkins Pipeline, Agile, Agile Workflow, SQL, PostgreSQL, Amazon CloudWatch, Amazon EC2, Amazon API Gateway, AWS CodeCommit, AWS CodeDeploy, Elastic APM, Jira, Amazon RDS, AWS IAM, MacOS, Safari, AWS DevOps, Git, Amazon Virtual Private Cloud (VPC), AWS CodePipeline, MySQL, Machine Learning, Site Reliability Engineering (SRE), Kibana, Logstash, PySpark, Python 3, Amazon Route 53, Load Balancers, Amazon Simple Notification Service (SNS), SSL, Cloud Deployment, Flask, Redis, Continuous Delivery (CD), GitLab CI/CD, AWS Cloud Architecture, Cloud Architecture, Cloud Infrastructure, RabbitMQ, Caching, NGINX, AWS Auto Scaling, Autoscaling, Autoscaling Groups, DevOps Engineer, Architecture, ECS, Containers, APIs, Load Testing, Firewalls, Shell Scripting, AWS ELB, IT Infrastructure, Solution Architecture, CloudOps, AWS Command Line Interface (CLI), Monitoring
Software Engineer
2016 - 2017
Systems limited
- Successfully delivered an eCommerce application for a leading store with 10,000 active monthly users.
- Reduced the bug backlogs by 90% in a time span of a week before going live.
- Improved the application performance by introducing caching for searched products by 40%.
Technologies: ASP.NET, ASP.NET MVC, Sitecore, JavaScript, Ajax, Software Testing, Bitbucket, Jenkins Pipeline, Agile, Agile Workflow, SQL, Jira, MacOS, Safari, Git, MySQL, Python 3, Load Balancers, Cloud Deployment, Continuous Delivery (CD), APIs
Experience
Presize AI
50% of fashion products are returned. 75% of them are due to the wrong size and bad fit. Fashion eCommerce shops lose money daily, and online shoppers are annoyed by returns.
Presize allows web shoppers to turn around in front of their smartphone camera once with normal clothes and automatically get their best-fitting clothing size recommended.
Presize allows web shoppers to turn around in front of their smartphone camera once with normal clothes and automatically get their best-fitting clothing size recommended.
PySpark Data Pipeline
An ETL pipeline used to aggregate the user conversion numbers from the daily usage data of the application.
The pipeline had three major parts: data extraction from ElasticSearch in the form of CSV files, Logstash was used to fetch the daily data into CSV, and it was stored in S3 buckets.
The second part was performing aggregations on hundreds of GBs of data to extract the numbers for the finance team.
The third and final part of the pipeline was pushing the aggregated numbers to ElasticSearch to show them in Kibana dashboards.
I completed this project from inception to completion while designing the infrastructure architecture, which included the scalable deployment of ElasticSearch on Kubernetes clusters while ensuring the system's security and scalability.
The pipeline had three major parts: data extraction from ElasticSearch in the form of CSV files, Logstash was used to fetch the daily data into CSV, and it was stored in S3 buckets.
The second part was performing aggregations on hundreds of GBs of data to extract the numbers for the finance team.
The third and final part of the pipeline was pushing the aggregated numbers to ElasticSearch to show them in Kibana dashboards.
I completed this project from inception to completion while designing the infrastructure architecture, which included the scalable deployment of ElasticSearch on Kubernetes clusters while ensuring the system's security and scalability.
Auction Application Scaling and Replication
I configured and managed multiple Beanstalk environments to handle increased traffic and demand. I implemented auto-scaling policies to ensure optimal utilization of resources and cost-effectiveness.
I implemented multi-region deployment strategies to ensure high availability and disaster recovery capabilities. I configured and maintained failover mechanisms to switch to a secondary region in case of a failure.
I established database replication strategies to ensure high availability and minimal downtime during maintenance and upgrades. I also monitored and troubleshot database replication issues and made necessary adjustments to improve reliability.
I integrated Datadog with the infrastructure to monitor and track the key performance metrics of the system and analyzed the container metrics. I identified performance bottlenecks.
I then collaborated with the development team to resolve technical challenges and improve the system's performance. I regularly monitored and analyzed the system's performance and made adjustments to ensure optimal operation.
I tested and validated the disaster recovery plan to ensure its effectiveness. Finally, I analyzed and evaluated the existing infrastructure for potential risks and failures.
I implemented multi-region deployment strategies to ensure high availability and disaster recovery capabilities. I configured and maintained failover mechanisms to switch to a secondary region in case of a failure.
I established database replication strategies to ensure high availability and minimal downtime during maintenance and upgrades. I also monitored and troubleshot database replication issues and made necessary adjustments to improve reliability.
I integrated Datadog with the infrastructure to monitor and track the key performance metrics of the system and analyzed the container metrics. I identified performance bottlenecks.
I then collaborated with the development team to resolve technical challenges and improve the system's performance. I regularly monitored and analyzed the system's performance and made adjustments to ensure optimal operation.
I tested and validated the disaster recovery plan to ensure its effectiveness. Finally, I analyzed and evaluated the existing infrastructure for potential risks and failures.
Education
2018 - 2021
Master's Degree in Computer Science
Technical University of Munich - Munich, Germany
2012 - 2016
Bachelor's Degree in Computer Science
National University of Computer and Emerging Sciences - Lahore, Pakistan
Skills
Libraries/APIs
Amazon EC2 API, Jenkins Pipeline, PySpark
Tools
CircleCI, Terraform, GitHub, Amazon Elastic Container Service (ECS), ELK (Elastic Stack), AWS CodeCommit, AWS CodeDeploy, Jira, AWS IAM, Git, GitLab CI/CD, NGINX, AWS ELB, CloudOps, AWS Command Line Interface (CLI), GitLab, Kubernetes HorizontalPodAutoscaler (HPA), Jenkins, Bitbucket, Amazon EKS, Amazon CloudWatch, Sentry, Amazon Virtual Private Cloud (VPC), Helm, Amazon Simple Notification Service (SNS), RabbitMQ, Azure Kubernetes Service (AKS), Logstash, Kibana, Elastic
Languages
Python, JavaScript, SQL, Python 3, Bash
Frameworks
Flask, Django, ASP.NET, ASP.NET MVC, Serverless Framework
Paradigms
DevOps, Continuous Integration (CI), Agile, Agile Workflow, Continuous Delivery (CD), Load Testing, Software Testing, Azure DevOps
Platforms
MacOS, Safari, Amazon Web Services (AWS), Docker, Kubernetes, Google Cloud Platform (GCP), Linux, Amazon EC2, Visual Studio Code (VS Code), AWS Lambda, Azure, AWS Elastic Beanstalk, Unix, Apache Kafka, AWS IoT
Storage
Amazon S3 (AWS S3), Datadog, Cloud Deployment, Elasticsearch, MySQL, Redis, PostgreSQL
Other
Containerization, Software Engineering, Operating Systems, Cloud Computing, Distributed Systems, CI/CD Pipelines, Container Orchestration, GitHub Actions, Infrastructure as Code (IaC), Elastic APM, Amazon RDS, AWS DevOps, Site Reliability Engineering (SRE), Amazon Route 53, Load Balancers, AWS Cloud Architecture, Cloud Architecture, Cloud Infrastructure, AWS Auto Scaling, Autoscaling, APM, DevOps Engineer, Architecture, ECS, Containers, APIs, Shell Scripting, Amazon API Gateway, AWS CodePipeline, SSL, Caching, Autoscaling Groups, Firewalls, HAProxy, IT Infrastructure, Solution Architecture, Monitoring, AWS ECS Fargate, Data Structures, Big Data, Sitecore, Ajax, Machine Learning, Cloud Services, Linux Administration, Application Performance Monitoring, Virtual Private Cloud (VPC), Prometheus, AIOps
Collaboration That Works
How to Work with Toptal
Toptal matches you directly with global industry experts from our network in hours—not weeks or months.
1
Share your needs
Discuss your requirements and refine your scope in a call with a Toptal domain expert.
2
Choose your talent
Get a short list of expertly matched talent within 24 hours to review, interview, and choose from.
3
Start your risk-free talent trial
Work with your chosen talent on a trial basis for up to two weeks. Pay only if you decide to hire them.
Top talent is in high demand.
Start hiring