Agentic AI Engineer

About the role

Engagement Type: Full-Time / Contractor

About CodeRoad

CodeRoad is a technology execution partner trusted by companies across North America since 2002. We work alongside our clients to engineer, modernize, and scale the technology that powers their businesses, combining deep technical expertise, specialized teams, and shared accountability for outcomes. Our capabilities span product engineering, platform engineering, legacy modernization, and AI engineering.

Our engineers, designers, delivery leaders, and technology specialists collaborate through Velocity Studios: specialized teams developed within CodeRoad to deliver complex technology initiatives. Being assigned to a project means you become part of a collaborative environment where you can strengthen your expertise, take ownership of meaningful work, and grow alongside experienced professionals.

Why Build Your Career at CodeRoad?

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High-Velocity Work & Client Exposure: We work close to the technology, teams and roadmaps that move real outcomes. Direct engagement with U.S. client teams and roadmaps.

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Career Continuity & Long-Term Opportunity: Your career here can outlast any single project. Continuous multi-project pipeline; long-tenure CodeRoaders.

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Engineering Autonomy & Technical Mastery: We invest in the skills that shape what's next. Agentic AI tools and training, Coursera Enterprise, Studio practice labs, L1-L7 Career Framework.

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High-Trust Employee Experience: You grow in an environment built on trust, transparency and recognition. High-touch onboarding, transparent leadership communication, community recognition, psychological safety.

Key Responsibilities

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Architect Multi-Agent Systems: Design and deploy hierarchical multi-agent workflows using LangGraph, transitioning legacy single-prompt applications into modular, autonomous systems.

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Optimize Token Economics & Latency: Build domain-specific sub-agents featuring dynamic, on-demand context-loading mechanisms to streamline operational security, performance, and token costs.

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Standardize Agent Tooling: Integrate scalable tool-calling capabilities and standardized connectors using the Model Context Protocol (MCP).

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Implement Observability & Cost Tracking: Manage and configure LangFuse to maintain complete visibility over LLM reasoning traces, performance metrics, and cost allocation.

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Establish Automated Evaluation Harnesses: Lead the creation of regression testing suites and automated eval harnesses using tools like PromptFlow or PromptFoo against key benchmark datasets.

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Enforce OWASP AI Security Guardrails: Collaborate with security leads to integrate strict access controls and guardrail frameworks aligned with OWASP Top 10 standards for LLM applications.

What You Bring