Business AI
5-agent orchestration layer on AWS Bedrock 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 Bedrock 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.
Rashanjot Kaur - AI Agent Architect
Designed 5-agent orchestration with MCP integration and dynamic tool selection.
6 open role(s)
Applied AI Engineer - Open
Build agent orchestration systems using Model Context Protocol. Design tool-use chains and multi-step reasoning workflows for market analysis.
Apply →Backend Engineer - Open
API design, async architecture, caching layers, and rate limiting. Build robust services that handle high-throughput agent workflows.
Apply →Frontend Engineer - Open
Analytics dashboards, interactive data visualizations, and real-time agent status displays. Turn raw market data into clear, actionable interfaces.
Apply →Data Engineer - Open
Web scraping pipelines, data normalization, and structured extraction from diverse market sources. Build reliable ingestion at scale.
Apply →AI Researcher - Open
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 - Open
Scaling infrastructure, observability, cost monitoring. Manage distributed agent workloads in production.
Apply →