Senior Software Engineer

Pearson, the world’s leading learning company, is hiring a Senior Software Engineer to play a key role on our AgentOps Engineering team. This hands-on role focuses on building AI agents and enhancing the platform capabilities to support enterprise operations.

Responsibilities:

• 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

Requirements:

• 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
• 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