Senior Software Engineer

Pearson • Durham, NC

3d • $95,000 - $160,000

About The Position

Pearson is hiring a Senior Software Engineer for AgentOps in the Office of the CTO. The role is focused on building agentic solutions that change how Pearson works and support a symbiotic workforce in which employees and AI agents collaborate. The engineer works directly with teams across Pearson to understand their domains, build agents into real workflows, and own delivery from discovery and architecture through deployment and live outcomes.

The position involves designing and shipping production-grade multi-agent applications, including tool use through MCP, agent-to-agent coordination, and appropriate handoffs to people. It also includes building generative and streaming interfaces, human-in-the-loop review, voice and speech-to-speech interaction, and multimodal input. Engineers assess emerging frameworks, models, and protocols, bring suitable innovations into production, and turn field experience into reusable patterns and platform capabilities.

The role emphasizes trust and enterprise readiness through traces, evaluations, guardrails, testing, SSO, permissions, data governance, observability, and reliability. The successful candidate collaborates with business teams and stakeholders, works with autonomy in a small team, and contributes to Pearson’s mission of helping people realize the life they imagine through learning.

Requirements

• At least 5 years of experience building and shipping production software, including recent hands-on experience designing, deploying, and operating agentic applications in production.
• Strong full-stack engineering skills across TypeScript, React, Next.js, Node, and Python, with sound architecture and system-design judgment.
• Hands-on experience with agentic frameworks and orchestration, such as LangChain, LangGraph, Deep Agents, CrewAI, Google’s Agent Development Kit, the Microsoft Agent Framework, OpenAI or Claude Agent SDKs, Vercel AI SDK, or equivalents; ability to assess trade-offs.
• Experience building agent interface layers, including generative and streaming UI with AG-UI, A2UI, CopilotKit, or similar, and human-in-the-loop interaction patterns.
• Production experience with multi-agent systems, MCP tool use, agent-to-agent coordination, evaluations, and guardrails.
• Practical experience with voice and multimodal AI, including speech-to-speech models, realtime audio, and vision, and judgment about their product value.
• Trace- and evaluation-driven approach to instrumenting agents, analyzing traces, and designing evaluations and guardrails based on evidence.
• Fluent daily use of agentic engineering tools such as Claude Code, Cursor, Codex, or Antigravity, with sound engineering judgment and review discipline.
• Customer-facing delivery skills, including discovery with business teams, tight scoping, and credible communication with engineers and senior stakeholders.
• Enterprise delivery experience with authentication and SSO, permissions, data governance, observability, and reliability at organizational scale.
• Working knowledge of retrieval, vector databases, and data engineering pipelines.
• Ability to define problems as well as solve them, collaborate effectively, and deliver quickly.

Responsibilities

• Embed with teams across Pearson to learn their domains and build agents into real workflows.
• Move from initial discovery to a working system in the field, then iterate with users.
• Own solutions end to end, including discovery, architecture, interface, deployment, and post-launch outcomes.
• Design and ship production-grade multi-agent systems using tools via MCP, agent-to-agent protocols, and appropriate human handoffs.
• Build usable agent interfaces, including generative and streaming UI, human-in-the-loop review, voice and speech-to-speech interaction, and multimodal input.
• Select and apply suitable agentic frameworks and SDKs for each problem.
• Instrument traces, build evaluation suites and guardrails, and catch regressions before users are affected.
• Deliver enterprise-ready systems with SSO, permissions, data governance, observability, and reliability.
• Evaluate emerging frameworks, models, and protocols and bring production-ready technologies into use.
• Use agentic engineering tools with strong review and testing discipline.
• Translate field learning into reusable patterns, components, and platform capabilities for AgentOps.
• Conduct discovery with business teams, scope work tightly, and communicate with engineers and senior stakeholders.

Benefits

• Competitive benefits designed to support employees and their families; details are available through Pearson’s benefits site.
• Eligibility to participate in an annual incentive program.
• Internal mobility and leadership development, with a path to broader strategic and executive roles.
• Visibility into the Chief Technology Office and engagement with the CTO and Technology Leadership Team.
• Ownership, autonomy, and the opportunity to work on zero-to-one agentic AI initiatives.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed