Pytorch, Agile, Scrum, Kubernetes

Tensorflow, Pytorch, Gcp, Azure, Python, AWS, Generative AI, Prompt engineering, Model monitoring, LLMs, scikit-learn, Drift detection, versioning, ML ecosystems, Experiment tracking, Fine-tuning adaptation techniques

containerization , Github, Unit Testing, Tensorflow, Numpy, Docker, Python, Scipy, Pandas, Kubernetes, Computer Vision, CI CD Pipelines, Multi-repo or Monorepo ML Architecture, scikit-learn, Cluster Creation, DevSecOps Automation, Model Deployment, ML Model Design Deployment, Edge AI Deployments, Open-source ML Tooling Contributions, Edge Deployment, Databricks Workflows, Real-time Analytics, Azure Cloud Architecture for ML, Edge Vision Deployments, ACR, Repo Management, Portainer

Tensorflow, Java, Distributed Computing, Machine Learning, MLops, Pytorch, Python, Sql, Text-to-SQL, large language models, AI systems

Tensorflow, Machine Learning, MLops, Pytorch, Natural Language Processing, Python, Sql, Deep Learning, Vector Databases, Generative AI, Retrieval-Augmented Generation

Machine Learning, Nlp, MLops, Databricks, Python, AWS, LangChain, Retrieval-based systems, Distributed ML pipelines, GenAI tooling, Feature engineering, LangGraph, Embeddings, Transformers, Amazon Bedrock, Model evaluation

Java, Natural Language Processing, data mining, Data Modeling, Tensorflow, Pytorch, Data Visualization, Keras, Python, Machine Learning Algorithms, deep learning models, R, Statistical Modeling, sci-kit, agentic frameworks

Java, Machine Learning, Tensorflow, Pytorch, Python, Large-scale ML systems, Agentic Systems, Knowledge retrieval, Maintenance, Model training, Feature engineering, Data pipelines, Distributed training, Inference optimizations, Monitoring