Senior Agentic AI Engineer (PCS786)

We're looking for a Senior Agentic AI Engineer to design, build, evaluate, and continuously improve sophisticated AI systems for a fast-moving enterprise AI platform. You will work across multi-agent architectures, context graphs, memory systems, model routing, and automated evaluation frameworks to build production systems that support complex, real-world business decisions. This is not an AI API integration or prompt-engineering role. We are looking for someone who thinks deeply about how models, agents, tools, context, memory, and evaluation systems work together and can turn that understanding into reliable production systems. The ideal engineer combines strong software engineering fundamentals with deep curiosity, experimentation, and hands-on experience building modern agentic AI systems. If that describes you, this role is a strong fit. Why You'll Want to Join You will be paid in USD (bi-monthly: every 15th and 30th) Paid Time Off in accordance with company policy Observance of Holidays per company guidelines 100% remote setup so you can work wherever you're most productive This role requires availability during US business hours High ownership over AI architecture and technical direction at an early, high-leverage stage Work directly with enterprise customers and see the real impact of what you build What You'll Work On Agentic AI Architecture Design and build production-grade agentic and multi-agent systems Architect specialized agents with clearly defined roles, tools, context, permissions, and decision boundaries Design orchestration and routing strategies across agents, tools, models, and workflows Build systems that intelligently use multiple LLMs and model providers depending on the task Determine which models should power specific agents based on quality, reasoning capability, reliability, latency, and cost Design failure handling, fallbacks, guardrails, and human-in-the-loop mechanisms Agent and Context Graph Interactions Design and manage how agents interact with context graphs, knowledge graphs, memory systems, and other context sources Determine what information and which portions of a context graph individual agents should be able to access Design context retrieval, filtering, ranking, and permission strategies based on each agent's role and task Build and evaluate interactions between agents and nested or interconnected context graphs Determine how much context an agent needs without unnecessarily increasing tokens, latency, or noise Design systems that dynamically provide agents with the most relevant context for a specific task Evaluate how changes to context availability affect agent accuracy, reasoning, reliability, and performance AI Evaluation and Reliability Design sophisticated evaluation frameworks for agentic AI systems Build evals across model, agent, and context graph interactions Systematically evaluate which models perform best for specific agents, tasks, and workflows Evaluate…