Senior Software Engineer at Pearson

Why This Role Matters

Are you passionate about building AI agents that reason, plan, route work, and run reliably in production—not just prototypes? Do you want to shape how an enterprise builds, operates, and governs digital workers at scale? Join our AgentOps Engineering team and help build the core platform capabilities that power agent crews running across the business.

This is a hands-on senior engineering role focused on building . You’ll ship the write-path features that let agents take real actions, design the orchestration and routing patterns that direct work to the right agent, and engineer enhancements to the platform itself—working inside it, not just on top of it. Through this, you’ll help teams across Pearson put dependable AI agents to work, and help people realize the lives they imagine through learning.

As a key member of the AgentOps Engineering team and Pearson, you will be expected to embody, and role model our leadership dimensions:

Live Our Purpose: You connect your engineering work to Pearson’s mission and make decisions that reflect a clear understanding of our strategy.

Simplify the Complexity: You navigate ambiguity in a fast-changing field and distil complex agentic systems into clear, reliable solutions.

Carry Our Culture: You collaborate generously, mentor through design and code reviews, and contribute to an inclusive, high-performing team.

Deliver Results: You set a high quality bar, get the details right, and own outcomes that matter to learners and stakeholders.

What You’ll Do

Build write-path features

Ship the features that let agents take real, governed actions—wrapping enterprise APIs, validators, and data sources so agents can write, not just read.

Design safe write semantics: idempotency, confirmation and human-in-the-loop gates, structured outputs, and audit-ready action logging.

Design multi-agent orchestration

Build multi-agent and digital-worker orchestration patterns that let specialized agents delegate, collaborate, and complete multi-step goals.

Build stateful, cyclic workflows using modern agent orchestration frameworks—enabling reflection, recovery, and adaptive execution beyond linear chains.

Engineer agent routing

Build the routing layer that directs requests to the right agent or crew—intent classification, capability-based dispatch, fallback, and escalation paths.

Create reusable components for retries, degraded modes, and human handoff; tune agent roles, goals, and prompts using fixtures and golden sets.

Enhance the platform

Work hands-on with the agentic platform—creating, versioning, invoking, and debugging crews, tasks, and graphs through platform APIs—and contribute enhancements back where new patterns are needed.

Design long-running, resumable workflows (checkpointing, persistence, context restoration) and resilience patterns for non-deterministic AI.

Ship with AI, at quality

Work primarily through AI-pair-programming as your default mode—researching, scaffolding, implementing, reviewing, and shipping at high velocity while holding quality through TDD and rigorous verification.

Raise the bar

Drive reusable engineering standards, shared libraries, and reference patterns; mentor engineers through design and code reviews; and share the prompts, skills, and workflows that multiply everyone’s output.

Who You Are

A hands-on senior engineer who builds production-grade agentic systems, not demos.

An independent owner who takes complex workstreams from ambiguity to reliable, shipped software.

A collaborator who influences through standards and mentoring, without needing authority.

Someone with strong taste and trade-off judgment across quality, latency, cost, resilience, and maintainability.

What You’ll Bring

(we hire for demonstrated skill, not years on a résumé)

You build LLM-powered systems, agents, or digital workers that run in production.

Strong Python and backend/platform engineering—async services, typed code, and clean architecture.

You work fluently with agent orchestration frameworks and can design, build, and orchestrate reliable agent tools—contracts, error handling, tool chaining.

You’ve built routing, dispatch, or workflow-control logic that directs work across components or services.

You use AI-pair-programming as a primary delivery mode without sacrificing quality.

Strong prompt engineering for structured outputs, nested schemas, and multi-agent coordination.

You read, edit, and contribute to a real platform codebase—APIs, runtime, storage—not just an SDK.

Solid APIs, distributed systems, and cloud-native engineering, with production-reliability instincts.

Even Better…

Experience evaluating agents—task success, groundedness, tool-use accuracy, schema conformance, and regression against golden fixtures.

Experience with tool/context interoperability protocols (such as MCP).

Experience with a major cloud platform (AWS preferred), containerization, and CI/CD; familiarity with state stores for orchestration and persistence.

AI observability and evaluation tooling for LLM systems.

RAG and memory patterns: vector databases, hybrid retrieval, re-ranking, and grounding.

Secure execution, sandboxing, and prompt-injection mitigation.

What You’ll Gain

A front-row seat building enterprise agentic AI infrastructure that teams across Pearson rely on.

Real ownership of orchestration, routing, and platform patterns others adopt.

A collaborative, inclusive, and innovation-driven culture.

Access to cutting-edge tools, platforms, and thought leadership.