Senior Engineering Manager / Principal Engineer, Smart Agents & Agentic Systems

Overview

Wand turns AI into labor. It enables humans and AI agents to operate together as a unified, hybrid workforce, with comprehensive management and oversight. And it’s already operating at scale inside some of the world’s largest organizations. Wand built the world’s first Agentic Labor Infrastructure enabling governments and global enterprises to create, manage, and scale digital workforces. Our mission is to integrate agent ecosystems into the core of work and business, unlocking a generational leap in the global economy. We’re building the infrastructure that lets humans and AI agents operate together safely, transparently, and at scale.

Position Summary

We are hiring a highly experienced Senior Engineering Manager / Principal Engineer – Smart Agents & Agentic Systems to lead the development of next-generation AI agents and the systems that power their capabilities. This role combines technical leadership with hands-on engineering, focused on building agentic infrastructure, skill frameworks, and autonomous systems that execute against product and business goals. You will be responsible for designing and scaling how AI agents are built, how they acquire and execute skills, and how they integrate into real-world product workflows. This includes developing low-level agent capabilities, embedding models into systems, and enabling agents to reason, plan, and act reliably. You will lead a team of engineers while remaining deeply involved in architecture and implementation. This role requires experience building agentic systems in production, with a strong understanding of how to operationalize AI agents as product features and business-critical systems. You will partner across product, data, and platform teams to deliver scalable, reliable, and goal-driven agentic platforms.

Role Responsibilities

• Lead the design and development of smart agent platforms, including how agents are created, orchestrated, and deployed.
• Define and implement systems for agent skill creation, composition, and lifecycle management.
• Build and evolve low-level agent infrastructure, including planning, reasoning, memory, and tool-use capabilities.
• Architect frameworks for embedding models into applications and enabling agents to interact with business logic and product workflows.
• Guide the development of agentic workflows that execute multi-step tasks aligned to product and business goals.
• Manage and mentor a team of engineers while contributing directly to critical system design and implementation.
• Collaborate with product and data teams to translate product requirements into agent capabilities and skills.
• Establish best practices for agent reliability, observability, evaluation, and performance optimization.
• Drive development of systems for agent evaluation, feedback loops, and continuous skill improvement.
• Ensure scalability and robustness of infrastructure supporting multi-agent systems and distributed execution.
• Contribute to CI/CD and development workflows supporting agent deployment, iteration, and lifecycle management.
• Troubleshoot complex issues across agent behavior, orchestration systems, and underlying ML infrastructure.
• Partner with senior leadership to shape agentic platform strategy and roadmap.

Key Requirements

• Extensive experience building production-grade agentic systems or AI platforms with autonomous capabilities.
• Strong hands-on expertise in low-level agent design, including planning, memory, tool use, and reasoning systems.
• Proven experience building systems for agent skill development, orchestration, and execution.
• Experience embedding machine learning models into real-world applications and product workflows.
• Strong programming skills (Python or similar) with experience building scalable backend systems for AI applications.
• Deep understanding of distributed systems, APIs, and real-time execution environments for agents.
• Experience designing and operating systems on cloud platforms such as AWS, Azure, or GCP.
• Experience managing and mentoring engineers while remaining deeply hands-on in system design and development.
• Strong debugging and problem-solving skills across complex agentic and ML systems.
• Ability to translate product goals into technical systems and agent capabilities.
• Strong communication skills and ability to collaborate across engineering, product, and data teams.

Preferred Experience

• Experience working at companies building agentic infrastructure, autonomous systems, or AI-native products.
• Experience with LLMs, NLP, generative AI, or multi-agent system design.
• Experience building agent frameworks, tool ecosystems, or skill-based architectures.
• Experience designing systems for agent memory, retrieval, and contextual reasoning.
• Experience with real-time systems, streaming architectures, and event-driven agent execution.
• Experience operating systems on Kubernetes or similar orchestration platforms.
• Experience building AI systems in enterprise SaaS or large-scale product environments.
• Experience designing evaluation systems for agent performance, alignment, and reliability.
• Experience working in regulated or enterprise environments deploying AI systems.

Personal Characteristics

• Strong systems thinker with deep understanding of agentic architectures, infrastructure, and product integration.
• High ownership mentality with accountability for delivery and reliability of agent-driven systems.
• Comfortable operating at both hands-on technical depth and team leadership level.
• Strong problem solver who anticipates failure modes in autonomous and distributed systems.
• Collaborative mindset with the ability to work across engineering, product, and data teams.
• Learning-oriented with a passion for advancing agentic AI and intelligent systems.
• Calm and methodical when diagnosing complex agent behavior and system-level issues.