MLops, Generative AI, Agentic technologies

software engineering practices , bedrock , Ml, Aws Services, MLops, Generative AI, AI workflows, AgentCore, architecture patterns, Agentic technologies, SageMaker, best practices

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

Machine Learning, ML Ops, DevOps practices, generative AI, cloud-based AI platforms, modern software engineering practices, enterprise AI deployment frameworks, AI technologies

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

Sql, Rest Apis, Python, data lake environments, enterprise data platforms, RAG embeddings, data pipelines, Agentic AI frameworks, autonomous workflow solutions, Generative AI solutions, document ingestion, AI data preparation techniques, enterprise integration patterns, ETL processes, AI orchestration platforms

snowflake , Machine Learning, Sklearn, Greenplum, Netezza, Pandas, Pytorch, Gcp, MLops, Spark, Microsoft Azure, Python, AWS, LangChain, Airflow, Generative AI, Feature Store, Teradata, Model Explainability, Model Deployment, E data, dbt, Observability