Amazon Web Services (AWS), MLops, AgentCore, SageMaker, Generative AI, Large-scale system design, Debugging inference pipelines, Agent orchestration, Agentic technologies, RAG architectures

Data Quality, MLops, Terraform, Docker, Python, Kubernetes, LLMOps, RAG, AI governance

Python, Devops, Docker, Git, MLops, vLLM, Cluster resource management tools, Retriever, SGLang, Model optimization techniques, NIM Factory Pipeline, CUDA optimization

Pytorch, MLops, Gcp, Docker, Azure, Kubernetes, Cloud-native engineering, Prompt engineering, Hugging Face ecosystem, AI deployment, Agentic AI frameworks, Structured tool calling, LLM evaluation and benchmarking, Generative AI application development, Workflow orchestration, RAG and knowledge retrieval systems, Inference optimization, Model fine-tuning or adaptation

bedrock , MLops, Code Review, Python, AWS, Generative AI, Step Functions, LLMOps, LLM integration, OpenSearch, Retrieval-Augmented Generation, Comprehend, SageMaker, document intelligence, Agentic AI, AI workflow orchestration, Textract, observability

Java, Apis, Tensorflow, MLops, Gcp, Pytorch, Docker, Spark, Keras, Azure, Kubernetes, Python, AWS, AI ML solutions, R, Dask

Tensorflow, Azure ML, Numpy, Pandas, Pytorch, Python, Machine Learning Algorithms, LangChain, Scikit-learn, tree-based models, MLOps practices, AWS SageMaker, regression techniques, Microsoft Copilot Studio, gradient boosting frameworks

Apis, Software Engineering, Generative AI technologies, AI infrastructure, Enterprise platform integrations, Technical Product Management, Technical Program Management