10/01/2026 updated
100 % available
AI & ML Engineer | +5 years, Infrastructure | 4 years
USA
Worldwide
Cloud EngineeringMachine LearningTensorFlowAzure Machine LearningDeep LearningGenerative AIMachine Learning OperationsTerraformArtificial Intelligence Markup Language (AIML)
I build and scale production AI/ML systems that help teams move from experimentation to reliable, real-world applications.
I combine AI/ML engineering, software development, cloud infrastructure, and MLOps to build systems that are scalable, reliable, and efficient. I can take an ML model from development through deployment, automation, monitoring, and production scaling.
My experience includes Machine Learning, Deep Learning, Generative AI, LLMs, NLP, model serving, ML pipelines, data pipelines, and AI infrastructure. I work with Python, PyTorch, TensorFlow, FastAPI, Docker, Kubernetes, Terraform, CI/CD, AWS, GCP, and Azure.
I help engineering teams:
Deploy AI models faster with automated ML and CI/CD pipelines
Scale AI workloads reliably across cloud and Kubernetes environments
Improve performance and infrastructure efficiency for production workloads
Build reliable AI services and APIs that integrate with existing applications
Automate repetitive ML and infrastructure tasks to reduce operational overhead
Monitor and troubleshoot production AI systems to maintain reliability
Build maintainable infrastructure that can support growing AI workloads
I’m particularly interested in the engineering challenges behind AI—making models easier to deploy, systems easier to operate, and infrastructure ready to scale.
What I bring to an employer: the ability to connect AI development with production engineering, helping teams turn promising ML models into reliable, scalable, and maintainable products.
Experience
Professional experience5 - 10 years
Freelancer since2019
Languages
EnglishGood
Project history
Designed and built infrastructure for production AI/ML systems. Worked on model deployment, inference, MLOps, Kubernetes, Docker, cloud infrastructure, CI/CD, monitoring, and automation. Focused on building scalable AI services, improving system performance, and making ML workloads reliable and efficient in production.
Built production AI applications using Generative AI, LLMs, and machine learning. Developed model-serving APIs, AI pipelines, and automated deployment workflows. Used Docker, Kubernetes, cloud platforms, and CI/CD to deploy and scale AI workloads while improving reliability and performance.
Developed machine learning and deep learning solutions using Python, PyTorch, and TensorFlow. Built data processing and ML pipelines for training, evaluation, and deployment. Developed APIs for integrating ML models into applications and worked on model optimization and automation.