software engineering practices , bedrock , MLops, SageMaker, Generative AI, AI workflows, AgentCore, Agentic technologies, Architecture Design

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

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

Pytorch, Gcp, MLops, Docker, Azure, Kubernetes, Cloud-native engineering, Agentic AI frameworks, Prompt engineering, AI deployment, Hugging Face ecosystem, 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

Networking, Operating Systems, cloud, Cybersecurity, Databases, Microservices, Cloud Computing, digital trust, LLMs, enterprise-level software development, Security Protocols, Ai, embodied AI, multimodal models, autonomous agents, Robotics, retrieval-augmented generation, human-machine interaction, data platforms, multi-agent orchestration, system architecture design, agentic AI, generative AI, physical AI systems, workflow automation

Python, MLops, GenAI solutions, RAG systems, AI tools, multi-agent workflows, GenMedia

Databricks, MLops, Gcp, AWS, Python, Azure, Apis, agent frameworks, OpenAI, GenAI, LLMOps, orchestration layers, embeddings, LangChain, RAG, CrewAI, Pinecone, LLM-based solutions, Mistral, LangGraph, RAG pipelines, Claude, vector stores, vector databases, Agentic AI, FAISS, Chroma