Business AI
multi-agent orchestration layer on AWS with Model Context Protocol integration and dynamic tool selection. Function-calling coordination across domain-specialized agents. Deployed across 4 geographic regions at 90% cost reduction.
5-agent orchestration system on AWS with MCP integration, deployed across 4 geographic regions. Dynamic tool selection at runtime, agent-to-agent coordination, and enterprise-grade analytical depth under sub-$500 cost constraints.
Operations in tier 2/3 regions lack access to data-driven decision support. Incumbent solutions cost $5K–$50K, creating a capability gap.
Specialized agent decomposition 5 domain-constrained agents operate within narrow scopes where smaller, cheaper models achieve equivalent quality. MCP integration enables dynamic access to external data sources.
Applied AI Engineer
Build agent orchestration systems using Model Context Protocol. Design tool-use chains and multi-step reasoning workflows for market analysis.
Apply →Backend Engineer
API design, async architecture, caching layers, and rate limiting. Build robust services that handle high-throughput agent workflows.
Apply →Frontend Engineer
Analytics dashboards, interactive data visualizations, and real-time agent status displays. Turn raw market data into clear, actionable interfaces.
Apply →Data Engineer
Web scraping pipelines, data normalization, and structured extraction from diverse market sources. Build reliable ingestion at scale.
Apply →AI Researcher
Research and benchmark agent orchestration patterns, evaluate tool-use strategies, and study scaling behavior of multi-agent market analysis systems. Design experiments and document findings.
Apply →DevOps / MLOps
Scaling infrastructure, observability, cost monitoring. Manage distributed agent workloads in production.
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