Senior Software Engineer

Build enterprise agentic AI platform capabilities on Pearson’s AgentOps Engineering team, with hands-on delivery across write paths, orchestration, and routing.

Responsibilities

• Ship write-path features that enable agents to perform real, governed actions by wrapping enterprise APIs, validators, and data sources.
• Design safe write semantics including idempotency, confirmation and human-in-the-loop gates, structured outputs, and audit-ready action logging.
• Implement multi-agent and digital-worker orchestration patterns so specialized agents can delegate, collaborate, and complete multi-step goals.
• Build stateful, cyclic workflows using modern agent orchestration frameworks to support reflection, recovery, and adaptive execution beyond linear chains.
• Create a routing layer that selects the right agent or crew using intent classification, capability-based dispatch, fallback, and escalation paths.
• Develop reusable components for retries, degraded modes, and human handoff; tune agent roles, goals, and prompts using fixtures and golden sets.
• Work directly in the agentic platform by creating, versioning, invoking, and debugging crews, tasks, and graphs via platform APIs, and contribute enhancements as new patterns emerge.
• Design long-running, resumable workflows with checkpointing, persistence, and context restoration, including resilience for non-deterministic AI.
• Deliver primarily through AI-pair-programming: researching, scaffolding, implementing, reviewing, and shipping quickly while maintaining quality with TDD and rigorous verification.
• Raise engineering standards via shared libraries and reference patterns; mentor through design and code reviews; share prompts, skills, and workflows that improve team output.

Requirements

• Hands-on senior engineer who builds production-grade agentic systems (not demos).
• Independent owner able to take complex work from ambiguity to reliable, shipped software.
• Collaborator who drives impact through standards and mentoring without relying on authority.
• Strong trade-off judgment across quality, latency, cost, resilience, and maintainability.
• Experience building LLM-powered systems, agents, or digital workers that run in production.
• Strong Python and backend/platform engineering skills (async services, typed code, clean architecture).
• Fluency with agent orchestration frameworks, including designing and orchestrating reliable agent tools (contracts, error handling, tool chaining).
• Built routing, dispatch, or workflow-control logic that directs work across components or services.
• Use AI-pair-programming as a primary delivery mode without sacrificing quality.
• Strong prompt engineering for structured outputs, nested schemas, and multi-agent coordination.
• Experience reading, editing, and contributing to a real platform codebase (APIs, runtime, storage), not only an SDK.
• Solid background in APIs, distributed systems, and cloud-native engineering with production reliability instincts.

Technologies

• AgentOps Engineering
• Python
• LLM-powered systems
• AI-pair-programming
• TDD
• Agent orchestration frameworks
• Async services
• Structured outputs
• Human-in-the-loop
• Enterprise APIs
• Tool chaining
• Checkpointing
• Persistence
• Context restoration

Benefits

• Access to cutting-edge tools, platforms, and thought leadership.
• Collaborative, inclusive, innovation-driven culture.
• Real ownership of orchestration, routing, and platform patterns others adopt.
• Front-row seat building enterprise agentic AI infrastructure relied on by teams across Pearson.
• Competitive benefits designed to support the diverse needs of employees and their families.
• Eligible to participate in an annual incentive program.

Even Better

• Experience evaluating agents (task success, groundedness, tool-use accuracy, schema conformance, 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 including vector databases, hybrid retrieval, re-ranking, and grounding.
• Secure execution, sandboxing, and prompt-injection mitigation.

What You’ll Gain

• Front-row seat building enterprise agentic AI infrastructure relied on by teams across Pearson.
• Real ownership of orchestration, routing, and platform patterns others adopt.
• Collaborative, inclusive, innovation-driven culture.
• Access to cutting-edge tools, platforms, and thought leadership.

Location and Compensation

• Location: Remote
• Salary: USD 90,000 - 135,000 per year

Application Details

• Applications accepted through 5th August 2026. Window may be extended depending on business needs.
• Full-time salary range is $90,000 – $135,000; compensation influenced by skill set, experience, and location.