Software Engineering Manager , AgentOps

Job Summary

Drive the future of Agentic AI at Pearson as part of the AgentOps team. Inspire innovation. Empower learning.

What This Role Matters Are you an Manager who leads from the front - still hands-on in the code, but at your best when you grow the people around you. Do you thrive at the intersection of platform engineering, orchestration, and operations? Join our AgentOps team and lead the group that builds and operates Pearsons internal agentic AI platform that runs agent crews across business units - with the reliability, observability, and governance that production demands. This is a genuine player-coach role. You will set technical direction by - designing systems, reviewing code, and shaping how we build - while you hire, coach, and deliver through a high-performing team. Without this role, agentic use cases stall between a promising prototype and a dependable production service, and the engineers building them lack the direction and support to do their best work. In this key position, you turn Pearsons AgentOps platform strategy into practical business outcomes and through this, help people realize the lives they imagine through learning.

What Youll Do

• Lead Grow the Team + Hire, coach, and develop a team of AgentOps Forward Deployed Engineers, owning performance, growth, and career progression.
• Foster an inclusive, high-performing culture of continuous improvement, strong engineering craft, and psychological safety.
• Set clear goals, run a healthy delivery cadence, and remove blockers so the team ships with quality and speed.
• Stay technically hands-on - lead design reviews and set the patterns and standards the team builds to.
• Take high-impact agentic use cases from prototype to production, with reusable connectors, shared services, and clean crew hand-offs.
• Establish standardized practices for agent development, orchestration, and LLMOps across the team.
• Instrument crews end to end with observability - traces, cost, tokens, latency, and error rates - so teams can see and trust what agents do.
• Own the reliability, performance, and cost targets that keep agentic workloads dependable, scalable, and operationally ready in production.
• Establish guardrails, constraints, and risk controls for agent-driven work - from acceptance criteria to safe, governed enterprise integration.
• Own the identity, authentication, and access model for agents and connectors - separating credentials, RBAC, and audit.
• Ensure agent crews meet regulatory, security, and data-privacy requirements as they scale across business units.
• Manage intake, prioritization, and cross-team dependencies, translating business needs into clear acceptance criteria for your team.
• Partner with product, research, cloud, data science, and engineering leaders across OCTO.
• Act as a trusted advisor to senior leadership on agentic AI capabilities, trade-offs, and operational risk.
• Stay ahead of emerging trends in agent orchestration, multi-agent frameworks, and LLMOps, and evaluate them pragmatically.
• Pilot new tools and patterns that raise the platforms capability, reliability, and developer experience.

Who You Are

• A player-coach who leads by doing - technically credible enough to earn your engineers trust, and genuinely energized by growing them.
• A pragmatic operator who turns complex agentic systems into reliable, production-ready services.
• A people leader who builds inclusive, high-performing teams and gives clear, kind, direct feedback.
• A collaborator who influences across functions and thrives in ambiguity.

What You Bring

• Selected on demonstrated capability, not years.
• Has led, coached, or mentored engineers - as a manager, tech lead, or team lead - and is ready to own hiring, performance, and growth for a team.
• Builds LLM-powered systems, AI agents, or workflow automation in production, and stays hands-on with the code.
• Strong Python and backend/platform engineering on cloud-native, distributed systems.
• Have worked on agent orchestration or multi-agent frameworks (e.g. CrewAI, LangGraph, ADK) and LLM observability tooling.
• Expertise in one of the public cloud platforms (AWS/Azure/GCP)
• Understands the agent lifecycle end to end - orchestration, identity/auth, observability, guardrails, and operating AI reliably in production.
• Sets standards and designs with clear trade-off awareness across quality, latency, cost, resilience, and maintainability.
• Excellent communication and stakeholder-engagement skills, from hands-on engineers to senior leadership.
• Bachelors or Masters in Computer Science, AI/ML, or a related field, or equivalent practical experience.
• Even better: Experience productionizing Agentic Applications
• Versed with MCP, A2A, context engineering, and HITL agent execution.
• AI observability and evaluation frameworks (Langfuse / LangSmith / Arize Phoenix).
• Fluent in AI-pair-programming (Cursor, Claude Code) as a delivery mode, and able to raise a teams floor with it.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.