Python or R, MLOps practices and tools

Python development, Kubernetes-based deployment, agentic reasoning patterns, RAG based architectures, Fast API, LLM and agent frameworks, REST API design and implementation, Containerisation, prompt engineering

Version Control, Api Development, Cloud Platforms, LLM Experience, Python for AI, AI ML Development, ML Frameworks, DevOps Deployment, Database Integration, Frontend Skills

Tensorflow, Pytorch, XGBoost, Software Engineering, Python, feature stores, experiment tracking, model versioning, MLflow, reinforcement learning, end-to-end ML pipelines, SageMaker, Kubeflow, Vertex AI

Java, Jenkins, Devops, Git, Gcp, MLops, Containers, Azure, Kubernetes, Python, AWS, Generative AI, event-driven services, agentic AI systems, ArgoCD

Tensorflow, Pytorch, XGBoost, Python, feature stores, experiment tracking, production-grade ML code, model versioning, MLflow, reinforcement learning, end-to-end ML pipelines, SageMaker, Kubeflow, Vertex AI

C, Tensorflow, Dsp, Pytorch, Python, compression techniques, ONNX, POSIX PTHREADS, RNNs, Audio Processing, Microcontroller architectures, ML frameworks, CNNs, fixed-point coding, model quantization, embedded solutions, audio framework, U-Nets, multi-threaded programming, Transformers

Microservices, Tensorflow, Numpy, Git, Pandas, MLops, Pytorch, Gcp, Spark, Rest Apis, Azure, Python, AWS, Scikit-learn

data engineering , Machine Learning, Deep Learning, Automation Tools, deploying models

Django, FastAPI, Microservices, Python, Flask, Distributed Systems, Machine Learning