Software Engineering Manager , AgentOps

• 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 platform's capability, reliability, and developer experience.

• 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.

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.
• Bachelor's or Master's 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 team's floor with it.

• The chance to lead a team building enterprise agentic AI used across Pearson.
• Room to stay technical while you grow as a people leader.
• Opportunities to shape strategy and influence outcomes across OCTO.
• Access to cutting-edge tools, platforms, and thought leadership.