DE architecting and engineering high-performance big data applications (AWS Glue, EMR, Kinesis, Athena, and Dynamo DB) and autonomous multi-agent systems; designing batch processing jobs and Extract, Transform, Load (ETL) pipelines to support predictive analytics using Hadoop, MongoDB, AWS, and PostgreSQL; developing agentic workflows using frameworks including Strands, CrewAI, LangGraph, and OpenAI Swarm with protocols -- Model Context Protocol (MCP) Server and Accelerated Graphics Port (AGP); and driving context aware predictive analytics and optimization models by implementing multi threaded, asynchronous solutions in Python and Java, supported by short term and long term memory management architectures for Retrieval Augmented Generation (RAG) pipelines using vector databases, OpenSearch, and high performance caching solutions (Redis or Memcached). DE implementing secure AI through adversarial robustness, federated learning, and governance across distributed ML systems using AI Generative Adversarial Network (AIGAN), Google Federated Learning Framework, SageMaker Clarify, and MLflow; enhancing low latency, high throughput model serving and maximizing central processing unit (CPU) or graphics processing unit (GPU) utilization through deployment on accelerated inference servers, using Deep Java Library (DJL), Triton, and Flask; and evaluating ML model inference performance using statistical analysis, monitoring tools (CloudWatch, Datadog, and Splunk), and dashboards including Streamlit and Gradio.