multi-agent orchestration frameworks, vector-based retrieval

Distributed Systems, Cloud Infrastructure, Python, Apis, Retrieval systems, Hallucinations, LLMs, Scalable backends, Agentic AI systems, Analysis, Scaling, System architecture for scalable agentic AI systems, External tools, Reliability, Engineering standards, Engineering stack, Action in financial workflows, Reasoning, AI systems architecture, Core AI platform, Evaluation guardrails, Tooling and deployment pipelines, Orchestration layers, AI agent workflows, Production-ready solutions, Agent-based systems

Python, LangChain, AI Agents, Vector databases, AI memory frameworks, LangFlow, Generative AI architectures, Agentic AI systems, LangGraph, semantic search, Embedding models, Tool calling frameworks, MCP Server implementation, Function calling, RAG pipelines, Memory sharing architectures, Multi-agent orchestration

Gcp, Databricks, Azure, Python, AWS, embeddings, LLaMA, Crew.ai, A2A Agent-to-Agent, Claude, model orchestration, prompt engineering, AutoGen, LangGraph, custom agent evaluation frameworks, MCP Model Context Protocol, Mistral, OpenAI, Gemini, LlamaIndex, synthetic datasets

production deployment , Tensorflow, Pytorch, Python, Version Control, Application Architecture, System Design, Containers, orchestration technologies, AI ML applications, CI CD, observability tools, code review practices, evaluation frameworks, LangChain, operational monitoring, Documentation, deep learning frameworks, retrieval-augmented generation, Semantic Kernel, Production Support, agent development patterns, model APIs, performance optimization, cloud ML platforms, prompt orchestration, LlamaIndex

Java, Regression Testing, Python, cost latency monitoring, vector search, orchestration patterns, tool function calling, trace inspection, structured output generation, AI systems, context engineering, agent frameworks, AI-native developer tools, agentic workflows, MCP Model Context Protocol, evaluation systems, LLM application patterns, RAG, reranking, prompt design, observability

LLMs, agent-based architectures, data science skills, enterprise data sources, hallucination analysis, retrieval, quality evaluation, cost monitoring, orchestration frameworks, prompt version tracking, testing and evaluation, vector databases, embeddings, human-in-the-loop feedback, observability, prompt optimization, model fine-tuning, agent-development platforms

Golang, Distributed Systems, Kafka, Typescript, Python, Java, Hadoop, Data Structures, Algorithms, Spark, Langchain, Flink, LLM serving and inference frameworks, LangGraph, MCP tool-calling, CrewAI, fine-tuning, secure agentic workflows, OAuth-based authentication, prompt engineering, AutoGen, Retrieval-augmented generation, agent orchestration, applied AI

Python, chatbots, image processing techniques, production-quality AI pipelines, LLMs, neural network architectures, agent orchestration, Azure OpenAI Service, deep learning frameworks, RAG architectures, Generative AI, vector databases