Model Context Protocol (MCP), Agent2Agent (A2A), Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Microsoft Azure, Python, GitHub Copilot, RESTful API design, AI ethics and regulations, AI frameworks

.Net Core, Ml, Java, API design, Sql, Typescript, Terraform, Azure, Python, AWS, GenAI, cloud-native applications, No-SQL, Ai, Claude Code, context engineering, GraphRAG, GitLab CI, GitHub Actions, RAG, CI/CD, LLM orchestration

Java, Databases, C#, Rust, C, C++, concurrency, Javascript, Distributed Systems, data structures, Python, query execution, Indexing, platform product areas, distributed algorithms, analytics systems

Rust, Sql, Data Modeling, Apache Airflow, C, Distributed Systems, Jira, Grafana, Python 3, Rest Apis, Linux, Prometheus, Git, AI coding assistants, Go, Cloud-native technologies, Micro-services, Data Analysis

FastAPI, Kubernetes, Python, Docker, PostgreSQL, agent memory systems, context management strategies, platform-level engineering standards, Ragas, prompt lifecycle management, LLM engineering, Azure OpenAI, RLHF, RAG pipeline architecture, LLM evaluation and observability practices, LangChain, pgvector, financial close Record-to-Report accounting, model fine-tuning, LangGraph