Teams at EchoStar

Lead and mentor an AI engineering team to design, architect, and deploy production-ready multi-agent, LLM-powered systems. Define technical strategy, set architecture and security standards, implement hybrid control flows/state machines, integrate agent-to-tool and agent-to-agent protocols, and collaborate with product and business teams to translate requirements into scalable microservices.

Designs, develops, and deploys enterprise-grade autonomous AI services and multi-agent workflows. Integrates MCP and A2A protocols, architectures hybrid control flows, secures tool execution, and builds scalable microservices. Applies SRE practices such as fallbacks, circuit breakers, audit trails, and observability. Requires advanced Python, cloud foundation model platforms, containerization, CI/CD, distributed systems, and production LLM deployment experience.

Lead development and architecture of Roku streaming client platform using BrightScript and SceneGraph. Own player state machines, DRM and ad integrations, performance profiling, memory tuning, and automated testing/benchmarking. Establish linting, static analysis, device-farm benchmarks, and AI-assisted workflows while coordinating cross-functional teams to deliver high-performance, certified Roku applications.