MLOps Engineer | Junior | TensorFlow, Terraform, Kubernetes

I am an AI/ML Engineer transitioning into MLOps, with a strong foundation in machine learning, deep learning, and computer vision, and hands-on experience in deploying AI models to production using cloud platforms. I have gained expertise in cloud infrastructure, virtual machines, networking, and deployment pipelines.

Currently, I am enhancing my skills in Kubernetes and Terraform, focusing on orchestrating containerized applications, managing infrastructure as code, and automating CI/CD pipelines. I have experience in building deployable AI systems using pretrained models, Docker containerization, and integrating CI/CD automation using GitHub Actions and Azure DevOps.

My technical skillset includes:

• Machine Learning & Deep Learning:

  • CNNs
  • ResNet
  • EfficientNet
  • Vision Transformers
  • PyTorch
  • TensorFlow.

• MLOps & Cloud:

  • Kubernetes
  • Terraform
  • Docker
  • Azure
  • CI/CD pipelines
  • model deployment
  • monitoring.

• Programming & Tools:

  • Python
  • Git
  • GitHub
  • GitHub Actions
  • VS Code
  • Jupyter Notebooks.

• Project Experience:

  • Developing AI applications such as plant disease detection (AgroLens AI), neural style transfer, and real-time computer vision solutions.

I am passionate about bridging the gap between AI research and production deployment, building scalable, reliable, and maintainable AI systems. My goal is to contribute as an MLOps Engineer in a forward-thinking organization where I can leverage my AI/ML expertise and cloud engineering skills to deliver production-ready solutions.

I am highly motivated, detail-oriented, and committed to continuous learning, with a strong focus on efficient, secure, and scalable deployment of AI models in real-world applications.