Staff Software Engineer, Agentops

Job Title: Staff Software Engineer

Job Description: Are you the engineer who sets where an agentic platform is going, proves the next wave of technology by building it, and lifts every engineer around you—while still shipping the hardest parts yourself? This role exists to do three things at once: architect the platform’s future state, bring cutting-edge and market-trending technology into Pearson through POCs and MVPs, and mentor the engineers who build alongside you.

Join our AgentOps Engineering team and shape the platform that powers agent crews running across the business. It’s a hands-on Staff-level role balanced across building, technical leadership, and proving new technology. You’ll define where orchestration, runtime, skills, and evaluation are heading—and prove your decisions with reference implementations and working prototypes, not just diagrams. Through this, you’ll help teams across Pearson put dependable AI agents to work, and help people realize the lives they imagine through learning.

This role aligns to industry job titles such as Agentops Engineer, Staff Engineer- Agentic Systems

Work Location/s: Bengaluru, India

Set the technical direction for Agentic AI at Pearson as part of the AgentOps Engineering team. Inspire innovation. Empower learning.

Pearson, the world’s leading learning company, is hiring a Staff Engineer to architect the future of our AgentOps Engineering platform, prove out emerging technology, and grow the engineers around them—while staying hands-on in the code.

Why This Role Matters

As a senior technical leader on the AgentOps Engineering team and at Pearson, you will be expected to embody, and role model our leadership dimensions:

• Live Our Purpose: You connect technical direction to Pearson’s mission and strategy, and help others see the same line.

• Simplify the Complexity: You set direction in a fast-changing field, distilling complex agentic systems into clear, reliable architecture others can build on.

• Carry Our Culture: You lead through design and code reviews, mentor engineers, and raise the bar without authority—building an inclusive, high-performing team.

• Deliver Results: You set the quality bar, own the hardest technical outcomes, and ship the patterns the platform depends on.

What You’ll Do

• Architect the future state

  • Own where the platform is going—define the target-state architecture for orchestration, runtime, skills and tools, and evaluation, and the roadmap of patterns and standards that gets there.

  • Make and document the hard trade-offs across quality, latency, cost, resilience, and maintainability; prove your decisions with reference implementations, not just diagrams.

• Scout and prove emerging technology

  • Track the fast-moving agentic-AI landscape—new frameworks, models, protocols, and techniques—and judge what’s worth Pearson’s investment.

  • De-risk the promising bets by building them: rapid POCs and MVPs that prove value, expose limits, and create a clear path from prototype to production.

• Design and build the platform

  • Design multi-agent and digital-worker orchestration patterns—delegation, cyclic and stateful workflows, reflection, recovery, and adaptive execution—and build the hardest pieces yourself.

  • Define how routing works across the platform—intent classification, capability-based dispatch, fallback, escalation—and the reusable components behind it.

  • Design long-running, resumable workflows (checkpointing, persistence, context restoration) and resilience patterns for non-deterministic AI; set the standards for safe agent write actions—idempotency, human-in-the-loop gates, structured outputs, audit-ready logging.

• Define skills, tools, and evaluation

  • Establish how skills and tools are built, exposed, discovered, versioned, and consumed, and drive tool/context interoperability standards across the platform.

  • Define how the platform evaluates agents—task success, groundedness, tool-use accuracy, schema conformance, regression against golden fixtures—and instrument deep tracing and observability.

• Mentor and raise the bar

  • Grow IC20–IC25 engineers through design and code reviews, pairing, and hands-on guidance; this is a core reason the role exists, not a side duty.

  • Multiply the team’s output through reusable patterns, shared libraries, and the prompts and workflows you put into everyone’s hands; make AI-pair-programming a high-leverage, high-quality default.

Who You Are

• A Staff-level engineer who sets technical direction and writes the hardest code—architecture without hands-on depth isn’t this role.

• A forward-looking architect who can see where the platform should go and prove the path with working prototypes.

• A mentor who grows other engineers and lifts the whole team’s bar—through influence, not authority.

• Someone with deep 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’ve designed and built production-grade agentic systems—architecture you proved by shipping it.

• You scout emerging technology and de-risk it through rapid POCs and MVPs that lead somewhere real.

• Deep Python and backend/platform engineering—async services, typed code, and clean, scalable architecture.

• You set patterns and standards others adopt: orchestration, agent tooling, runtime, and resilience for non-deterministic AI.

• You design and build agent orchestration and routing across frameworks—delegation, retries, fallback, structured outputs, human-in-the-loop.

• You own a real platform codebase end-to-end—APIs, runtime, storage—and raise its engineering bar.

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

• You mentor engineers and lead through design and code reviews, without managing them.

Even Better…

• Experience defining evaluation strategy for agentic systems—groundedness, tool-use accuracy, regression suites.

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

• Deep experience with a major cloud platform (AWS preferred), containerization, CI/CD, and 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 setting the technical direction of enterprise agentic AI infrastructure teams across Pearson rely on.

• A mandate to explore the frontier of agentic AI and bring the best of it into production.

• Real ownership of the architecture, standards, and patterns that define the platform.

• A collaborative, inclusive, and innovation-driven culture, with access to cutting-edge tools and thought leadership.

Why Pearson?

At Pearson, we don’t just build careers—we accelerate them. Known for our strong culture of internal mobility and leadership development, we offer a clear path to broader technical leadership.

We believe great work deserves great rewards. That’s why we offer some of the most competitive benefits in the industry—designed to support the diverse needs of our people and their families. Explore our Benefits.

At Pearson, we’re reimagining learning through technology. If you set the technical bar, build with craft, and want to shape how an enterprise puts AI agents to work at scale—we’d love to hear from you.

Apply today and imagine the impact you can make.