Agent Architect

Responsibilities

• Design blueprints for agent workflows, including reasoning loops, tool integration, memory systems, and multi-agent orchestration.
• Architect scalable, modular, and extensible agentic systems aligned with enterprise needs.
• Design context pipelines using RAG, knowledge graphs, APIs, and multi-tiered memory systems.
• Build frameworks to connect LLMs, APIs, enterprise data sources, and observability layers.
• Implement guardrails for safety, compliance, and Responsible AI governance standards.
• Guide AI engineers and AgentOps specialists in best practices for agent design and trade-off evaluation.

Requirements

• 6–10 years of experience in AI/ML engineering, systems architecture, or enterprise software design.
• Deep knowledge of LLM architectures and orchestration frameworks such as LangChain, LangGraph, or LlamaIndex.
• Strong understanding of context engineering, RAG pipelines, and vector/knowledge databases.
• Experience with multi-agent orchestration frameworks (e.g., MCP, A2A messaging, AgentBridge).
• Proficiency in Python and modern AI development stacks.
• Familiarity with cloud-native architecture (AWS, GCP, Azure) and container orchestration (Kubernetes, Docker).

Preferred Qualifications

• Background in Reinforcement Learning (RLHF, RLAIF, reward modeling).
• Experience with enterprise-scale deployments in regulated industries like BFSI or GRC.
• Prior experience as a Solutions Architect, AI Architect, or Technical Lead.
• Contributions to open-source frameworks in multi-agent systems or LLM orchestration.

About the Company

XenonStack is a fast-growing Data and AI Foundry for Agentic Systems. We specialize in designing, deploying, and scaling enterprise-grade AI agents that are trustworthy, explainable, and production-ready. Our mission is to accelerate the world’s transition to AI + Human Intelligence through our specialized platforms in Vision AI, AgentOps, and Inference Infrastructure.