Muhammad Anees Tahir, Developer in Munich, Bavaria, Germany
Muhammad is available for hire
Hire Muhammad

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

BT&M Investments LLC dba Qtego Fundraising Services
AWS, Amazon EC2, AWS RDS, AWS Elastic Beanstalk, Datadog, Cloud Services, Bash...
SimplyWise, Inc.
Kubernetes, Helm, Terraform, Amazon EKS, Django...
Presize GmbH
Cloud Engineering, Azure, Kubernetes, CircleCI, CI/CD Pipelines, Docker...

Experience

Availability

Part-time

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

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: AWS, Amazon EC2, AWS RDS, AWS Elastic Beanstalk, Datadog, Cloud Services, Bash, Unix, Linux Administration, DevOps, Architecture, Containers, APIs, Load Testing, System Security, Shell Scripting, HAProxy, AWS ELB, Solution Architecture, CloudOps, AWS 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, Agile Development, AWS Cloud, Cloud Architecture, Cloud Infrastructure, Nginx, Autoscaling, APM, DevOps, Architecture, ECS, Containers, APIs, Load Testing, Shell Scripting, HAProxy, AWS ELB, Solution Architecture, CloudOps, AWS CLI, Monitoring, Application Performance Monitoring

DevOps 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). 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.
  • Assured high up-times for our system and low response times. Maintained 99.99% uptime as service-level agreements (SLA).
Technologies: Cloud Engineering, Azure, Kubernetes, CircleCI, CI/CD Pipelines, Docker, Terraform, GitHub, Kubernetes HorizontalPodAutoscaler (HPA), GitHub Actions, Linux, AWS, Containerization, Visual Studio Development, Elasticsearch, DevOps, Python, Amazon Elastic Container Service (ECS), Container Orchestration, Git, Jenkins, Continuous Integration (CI), AWS Lambda, Amazon S3, ELK (Elastic Stack), Azure DevOps, Infrastructure as Code (IaC), Bitbucket, Agile Development, Agile Development, SQL, PostgreSQL, Amazon EKS, Azure Kubernetes Service (AKS), AWS, Amazon EC2, Amazon API, Datadog, Sentry, AWS, AWS, Elastic APM, Jira, AWS RDS, AWS IAM, MacOS, Safari, AWS DevOps, Git, Amazon Virtual Private Cloud (VPC), AWS, MySQL, Machine Learning, Helm, Site Reliability, Kibana, Logstash, PySpark, Python, Amazon Route 53, Load Balancers, AWS, SSL, Django, Cloud Deployment, Flask, Redis, Agile Development, GitLab CI/CD, AWS Cloud, Cloud Architecture, Cloud Infrastructure, RabbitMQ, Caching, Nginx, AWS Auto Scaling, AWS, Autoscaling, Autoscaling Groups, APM, DevOps, Architecture, ECS, Containers, APIs, Load Testing, System Security, Shell Scripting, HAProxy, AWS ELB, IT Infrastructure, Solution Architecture, CloudOps, AWS CLI, Monitoring

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, AWS, Containerization, Visual Studio Development, Python, Amazon Elastic Container Service (ECS), Container Orchestration, Continuous Integration (CI), AWS Lambda, Amazon S3, Infrastructure as Code (IaC), Agile Development, Agile Development, SQL, Amazon EKS, AWS, Amazon EC2, Amazon API, AWS, AWS, AWS RDS, AWS IAM, MacOS, Safari, AWS DevOps, Git, Amazon Virtual Private Cloud (VPC), AWS, Python, Amazon Route 53, Load Balancers, AWS, SSL, Cloud Deployment, Redis, Agile Development, GitLab CI/CD, AWS Cloud, Cloud Architecture, Cloud Infrastructure, RabbitMQ, Caching, Nginx, AWS Auto Scaling, AWS, Autoscaling, Autoscaling Groups, APM, DevOps, Architecture, ECS, Containers, APIs, AWS ELB, IT Infrastructure, Solution Architecture, CloudOps, AWS 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, AWS, Containerization, Visual Studio Development, DevOps, Container Orchestration, Continuous Integration (CI), AWS Lambda, Amazon S3, ELK (Elastic Stack), Infrastructure as Code (IaC), Bitbucket, Agile Development, Agile Development, SQL, PostgreSQL, Amazon EKS, AWS, Amazon EC2, Amazon API, Datadog, Sentry, AWS, AWS, Elastic APM, Jira, AWS RDS, AWS IAM, MacOS, Safari, AWS DevOps, Git, Amazon Virtual Private Cloud (VPC), AWS, MySQL, Site Reliability, Kibana, Python, Helm, Amazon Route 53, Load Balancers, AWS, SSL, Cloud Deployment, Flask, Redis, Agile Development, GitLab CI/CD, AWS Cloud, Cloud Architecture, Cloud Infrastructure, RabbitMQ, Caching, Nginx, AWS Auto Scaling, AWS, Autoscaling, Autoscaling Groups, APM, DevOps, Architecture, Containers, APIs, Load Testing, System Security, Shell Scripting, HAProxy, AWS ELB, IT Infrastructure, Solution Architecture, CloudOps, AWS 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 Architecture, AWS, Containerization, Visual Studio Development, Elasticsearch, Amazon Elastic Container Service (ECS), Container Orchestration, Git, Jenkins, Continuous Integration (CI), Amazon S3, ELK (Elastic Stack), Infrastructure as Code (IaC), Bitbucket, Jenkins Pipeline, Agile Development, Agile Development, SQL, PostgreSQL, AWS, Amazon EC2, Amazon API, AWS, AWS, Elastic APM, Jira, AWS RDS, AWS IAM, MacOS, Safari, AWS DevOps, Git, Amazon Virtual Private Cloud (VPC), AWS, MySQL, Machine Learning, Site Reliability, Kibana, Logstash, PySpark, Python, Amazon Route 53, Load Balancers, AWS, SSL, Cloud Deployment, Flask, Redis, Agile Development, GitLab CI/CD, AWS Cloud, Cloud Architecture, Cloud Infrastructure, RabbitMQ, Caching, Nginx, AWS Auto Scaling, Autoscaling, Autoscaling Groups, DevOps, Architecture, ECS, Containers, APIs, Load Testing, System Security, Shell Scripting, AWS ELB, IT Infrastructure, Solution Architecture, CloudOps, AWS 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 Development, Agile Development, SQL, Jira, MacOS, Safari, Git, MySQL, Python, Load Balancers, Cloud Deployment, Agile Development, APIs

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.

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.

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.
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

Libraries/APIs

AWS, Jenkins Pipeline, PySpark

Tools

CircleCI, Terraform, GitHub, Amazon Elastic Container Service (ECS), ELK (Elastic Stack), AWS, AWS, Jira, AWS IAM, Git, GitLab CI/CD, Nginx, AWS ELB, CloudOps, AWS CLI, Git, Kubernetes HorizontalPodAutoscaler (HPA), Jenkins, Bitbucket, Amazon EKS, AWS, Sentry, Amazon Virtual Private Cloud (VPC), Helm, AWS, RabbitMQ, Azure Kubernetes Service (AKS), Logstash, Kibana

Languages

Python, JavaScript, SQL, Python, Bash

Frameworks

Flask, Django, ASP.NET, ASP.NET MVC

Paradigms

DevOps, Continuous Integration (CI), Agile Development, Agile Development, Agile Development, Load Testing, Software Testing, Azure DevOps

Platforms

MacOS, Safari, AWS, Docker, Kubernetes, Cloud Engineering, Linux, Amazon EC2, Visual Studio Development, AWS Lambda, Azure, AWS Elastic Beanstalk, Unix, Apache Kafka

Storage

Amazon 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, AWS RDS, AWS DevOps, Site Reliability, Amazon Route 53, Load Balancers, AWS Cloud, Cloud Architecture, Cloud Infrastructure, AWS Auto Scaling, Autoscaling, APM, DevOps, Architecture, ECS, Containers, APIs, Shell Scripting, Amazon API, AWS, SSL, Caching, Autoscaling Groups, System Security, HAProxy, IT Infrastructure, Solution Architecture, Monitoring, Data Structures, Big Data Architecture, Sitecore, Ajax, Machine Learning, Cloud Services, Linux Administration, Application Performance Monitoring

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