Job DescriptionRole Overview:We are seeking a highly skilled,
hands-on Lead AI Systems Architect to design, build, and scale our next-generation AI ecosystem. In this role, you will lead the architecture and implementation of
Agent-to-Agent frameworks, Model Context Protocol (MCP) integrations, and our proprietary AI development platform.
This is not a purely theoretical role. You must be deeply technical, actively writing code, and capable of translating complex AI research into robust, production-grade software. As a key bridge between engineering teams and stakeholders, you must also possess
exceptional communication skills to articulate technical vision and drive cross-functional alignment.
Core Responsibilities:- Agent-to-Agent Architecture: Design and implement multi-agent orchestration frameworks, focusing on autonomous negotiation, task delegation, state management, and secure inter-agent communication.
- MCP Integration: Architect and build scalable Model Context Protocol (MCP) servers and clients to securely connect LLMs to data sources, development tools, and enterprise environments.
- AI Platform Development: Drive the architectural vision for our internal AI development platform, ensuring high throughput, low latency, and seamless developer workflows for training, testing, and deploying models.
- Hands-on Engineering: Write high-quality, production-ready code, build prototypes, and establish engineering best practices for AI application development.
- Technical Leadership & Communication: Standardize architectural patterns across the organization. Mentor junior engineers and explain complex technical concepts to non-technical stakeholders clearly and effectively.
Required Qualifications & Skills- Experience: 10+ years of software architecture experience, with at least 3+ years of dedicated, hands-on experience building and deploying production-grade LLM applications and AI systems.
- AI & Agentic Expertise: Deep understanding of multi-agent frameworks (e.g., LangGraph, AutoGen, CrewAI), prompt engineering, RAG pipelines, and vector databases.
- Protocol & API Mastery: Proven experience with Model Context Protocol (MCP) or designing similar context-sharing and tool-calling protocols for GenAI.
- Technical Stack: Proficiency in Python, TypeScript, or Go, along with cloud native technologies (AWS/GCP/Azure, Kubernetes, Docker) and modern AI SDKs.
- Communication: Outstanding verbal and written communication skills, with a proven ability to lead technical discussions and author comprehensive architecture RFCs.
- Education: College/University degree or higher
Responsibilities- Design and implement multi-agent orchestration frameworks with autonomous negotiation, task delegation, and secure inter-agent communication.
- Architect, develop, and operate scalable MCP servers and clients that connect LLMs to data sources, tools, and enterprise environments.
- Define the technical roadmap for the internal AI development platform, focusing on throughput, latency, and seamless CI/CD pipelines.
- Write production-grade code, create prototypes, and establish engineering best practices for AI application development.
- Standardize architectural patterns, guidelines, and reusable components across the organization.
- Mentor junior engineers and foster a culture of continuous learning in AI and agentic technologies.
- Collaborate with product, data, and security teams to align AI solutions with business objectives and compliance requirements.
- Lead technical discussions, produce detailed architecture RFCs, and communicate complex concepts to non-technical stakeholders.
- Evaluate and integrate emerging AI SDKs, vector databases, and cloud-native services (AWS/GCP/Azure, Kubernetes, Docker).
- Monitor system performance, conduct root-cause analysis, and drive continuous improvement of AI services.
Qualifications- 10+ years of software architecture experience, including 3+ years building production-grade LLM applications.
- Deep expertise in multi-agent frameworks such as LangGraph, AutoGen, or CrewAI.
- Hands-on experience with prompt engineering, Retrieval-Augmented Generation (RAG) pipelines, and vector databases.
- Proven track record designing or implementing Model Context Protocol (MCP) or similar context-sharing protocols.
- Strong programming skills in Python, TypeScript, or Go.
- Extensive experience with cloud-native technologies (AWS/GCP/Azure, Kubernetes, Docker).
- Demonstrated ability to create high-throughput, low-latency AI platforms.
- Excellent verbal and written communication; ability to author comprehensive architecture RFCs.
- Leadership experience mentoring engineers and driving cross-functional technical alignment.
- Bachelor's or higher degree in Computer Science, Engineering, or a related field.
