data engineering , Technical Leadership, python, Pytorch, Deep Learning, Machine Learning, Numpy, MLops, Azure, retrieval-augmented generation, vector search, langchain, mentorship, model deployment, embeddings, langgraph, llmops, agentic ai, production integration, model serving, prompt engineering, pipeline architecture

Data Architecture for AI ML, Agent Core, Claude LLM Integration, RAG Retrieval-Augmented Generation, AI ML Workload Optimization, Amazon Bedrock, AWS SageMaker

ROCm stack components (HIP, GPU programming models (HIP, Opencl, C++, Cuda, Python, ROCr, Multi-GPU systems, Test orchestration and reporting tools, AI tools in validation and testing, Linux-based development and debugging, Drivers, Catch2, compilers, CUDA runtime concepts, HIP runtime, Graph APIs

.NET, Angular, Apis, AI integration

System Design, Testing, Python, Application Development, Cloud-native deployment patterns, Operational stability for ML or data-intensive systems

Static timing analysis, Splunk, Unix, AI ML, Linux, Lsf, Python, Bitbucket, Jenkins, Git, DesignSync, EDA Tools, make, GenAI, Convolutional Networks, CI CD process, SW development and debug tools, Design Verification, reinforcement learning, Graph Neural Networks, Dft, Design revision control, Power-aware flows

Application Development, System Design, Testing, Python, Cloud-native deployment patterns, Operational stability for ML or data-intensive systems