Machine Learning Python Developer
Resume:
SUMMARY
AMC Networks – Senior Python Developer (remote) May 2015 – Apr 2020
Confluence – Principal Python Developer (remote) Jan 2023 - Present Experienced ML/AI DevOps Engineer with a strong foundation in Python, cloud technologies (AWS, GCP, Azure), and modern DevOps tools (Docker, Kubernetes, Jenkins). Over 15 years of software development experience, specializing in automating machine learning workflows, integrating DevOps with AI/ML operations, and driving high-performance solutions. Proven track record in deploying scalable AI models and supporting CI/CD pipelines for machine learning projects. TECHNICAL SKILLS
PROFESSIONAL EXPERIENCE
Languages:
Python, JavaScript, C++
ML/AI Libraries:
TensorFlow, PyTorch, Scikit-learn,
Keras
DevOps Tools:
Docker, Kubernetes, Jenkins,
Terraform,Ansible
Cloud Platforms: AWS, GCP, Azure
Infrastructure as Code (IaC): Terraform,
CloudFormation
CI/CD: Jenkins, GitLab CI, CircleCI
Database Technologies: PostgreSQL,
MySQL,
MongoDB, Redis
Additional: REST APIs, Microservices, Agile,
Test-Driven Development (TDD), MLOps
ABID YASIN
ML/AI DEVOPS ENGINEER
Long Island, New York, United States
************@*****.***
QuantumScape – Python Developer Apr 2012 – Mar 2015 Lead a team of 20+ developers in designing and developing scalable web applications using Python and related technologies.
Architect and implement microservices-based solutions to enhance system performance and scalability. Drive the adoption of best practices in code quality, testing, and documentation. Mentor junior developers and conduct regular code reviews to ensure high standards. Collaborate with cross-functional teams to define project requirements and deliver robust solutions. Key projects:
Developed a high-performance data analytics platform, reducing data processing time by 40%. Designed and deployed a scalable e-commerce solution handling millions of transactions daily. Developed and maintained web applications using Django and Flask. Implemented RESTful APIs and integrated third-party services.
Optimized database queries and improved application performance. Conducted unit testing and implemented continuous integration pipelines. Key projects:
Built a real-time inventory management system, improving accuracy and efficiency. Created a customer feedback analysis tool, leveraging machine learning to derive insights from data. Web Application Development: Designed and developed scalable web applications using Python frameworks such as Django and Flask, enhancing user engagement by 25% and improving system performance by 20%. Automation of Processes: Created and maintained automated scripts to streamline routine tasks, resulting in a 30% reduction in manual effort and a 15% increase in operational efficiency. Stakeholder Collaboration: Worked closely with stakeholders to gather and analyze requirements, ensuring the delivery of solutions that met business objectives and user needs. Documentation: Authored and maintained comprehensive documentation for software solutions, facilitating easier onboarding and knowledge transfer within the team. Key Projects:
Automated Reporting System: Developed an automated reporting system using Python and SQL, reducing manual reporting time by 60% and improving data accuracy for key stakeholders.Web-Based CRM System: Implemented a web-based Customer Relationship Management (CRM) system, enhancing customer relationship management and contributing to a 15% increase in sales.
Publications / Blogs / Talks:
“Optimizing ML Pipelines with DevOps Practices” – Published an article on Medium discussing how DevOps principles can be applied to machine learning workflows to improve efficiency and model deployment cycles.
"Automating AI Deployments in the Cloud" – Presented at KubeCon 2024, discussing how to effectively manage and deploy generative AI models using Kubernetes and Terraform. Tools & Technologies Used:
ML/AI: TensorFlow, PyTorch, Scikit-learn, Hugging Face, OpenAI GPT, GANs, VAEs DevOps: Jenkins, Docker, Kubernetes, Helm, Terraform, Ansible Cloud: AWS, Google Cloud, Azure, Lambda, SageMaker Data Pipelines & Automation: Apache Airflow, Apache Kafka, MLflow Generative AI: GPT-3, DALL-E, GANs, StyleGAN, Stable Diffusion Collaboration & Version Control: Git, GitHub, GitLab, Bitbucket AI-Powered Recommendation System
Developed and deployed a scalable recommendation engine using TensorFlow and AWS Lambda, improving user engagement by 30%. Integrated it into the existing Kubernetes-based infrastructure to handle millions of daily requests.
Generative AI for Text Creation
Led the deployment of a GPT-3 based chatbot for automating customer support, significantly improving response times and customer satisfaction. The solution utilized AWS SageMaker for model training and deployment, integrated with Jenkins CI/CD pipelines for continuous updates. Automated Machine Learning Pipeline for Model Retraining Designed a fully automated machine learning pipeline using Kubeflow and Apache Airflow. This system retrains models based on real-time data, ensuring up-to-date performance and lowering manual intervention by 70%.
Generative Art with GANs
Developed a Generative Adversarial Network (GAN) model for creating high-quality artwork. Deployed the model in a Dockerized environment for real-time inference, utilizing Kubernetes for scaling based on demand.
KEY POINTS
BachelorofComputerScience
EDUCATION
CERTIFICATIONS
AWS Certified Solutions Architect – Professional
Certified Kubernetes Administrator (CKA) Certified ScrumMaster (CSM) Google Cloud Professional Data Engineer
TensorFlow Developer Certificate
New York University (NYU)
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