Staff Software Engineer, AI-Driven Field Delivery

Pearson; Indianapolis (IN); On-site; USD 140,000 - 190,000; Full time

Job summary

Pearson's PSG-CP team seeks a forward deployed engineer who sits with customers and writes code that ships. You will own end-to-end systems, from discovery to production, in fast-moving engagements. Expect deep frontend and backend work, strong CS fundamentals, and extensive AI/LLM usage across real workflows.

You’ll embed with internal business lines, operate autonomously, and drive AI adoption at scale. This is a high-ownership, high-impact role within Pearson.

Qualifications

• 8+ years of full-stack engineering experience, with meaningful time spent shipping production systems end to end. Background as a technical founder, FDE, or software engineer with consulting experience is all fair game.
• Strong CS fundamentals. Data structures, algorithms, system design. You can whiteboard a service architecture, talk through tradeoffs, and not flinch at a coding interview.
• Frontend depth. Modern React, TypeScript, real component architecture, state management you can defend, and the taste to build something that looks finished — not just functional.
• Backend depth in order of importance (Node.JS, Java, and Python). API design, data modeling, auth, error handling. Comfortable owning a service from request handler to schema.
• Production LLM experience. Advanced prompt engineering, agent development, evaluation frameworks, retrieval, and deployment at scale. You've shipped at least one real LLM-backed application that someone other than you used.
• Fluency with AI-assisted development tools and agentic coding. This is not a side note. The work moves at a pace that assumes you're using these tools efficiently, accurately, and rapidly. You should be able to demonstrate a clear process that delivers measurable results.
• Data fluency. Databases at a real working level, comfort with Python data tooling.
• Cloud and deployment fluency. AWS, GCP, or Azure — enough to put a service on a real URL behind real auth without filing a ticket. CI/CD, containers, observability.
• Integration experience. You've built things that talk to systems you didn't write. SSO, OAuth, third-party APIs, internal platforms, legacy databases.
• High agency. You navigate ambiguity inside a complex organization without needing someone to clear your path. You make calls and own them.
• High cooperation, low ego. Pearson is 30,000+ people. Forward deployed work crosses team lines constantly.
• Communication skills that work in both directions. You can sit in a room of executives and engineers, hold both threads at once, and not lose either audience. You can speak to end users about their problem and translate that into something other engineering teams can actually build against.
• Currency on what's changing. You stay close to the frontier of LLM capabilities, agent patterns, and AI product stacks.
• Education: Bachelor’s degree in Computer Science or equivalent combination of education, training, and professional experience

Responsibilities

• Going where the problem is. You will work directly with the internal stakeholders inside our different lines of business. Through real understanding of their pain points, their workflows, and the architecture of their current technical landscape, you'll design and build systems end to end for them.
• Conducting real discovery. You're not waiting for a finished brief. You sit with users, watch them work, ask the questions, and translate what you hear into something buildable. The discovery is part of the engineering job, not a step that happens before it.
• Building during the conversation. The first version often exists before the meeting ends. People react to working software very differently than they react to wireframes — your job is to give them something to react to as fast as possible.
• Working from messy inputs. Spreadsheets, PDFs, screenshots, recorded calls, half-written documents, contradictory emails from two stakeholders. You read all of it and turn it into something coherent.
• Shipping production AI applications. Beyond prototypes, you build real applications on top of frontier LLMs, agents, MCP servers, evaluation harnesses, retrieval systems, custom skills — that run in production against real workflows.
• Iterating fast across many engagements. A typical engagement runs through many versions in a short window. Requirements shift every cycle.
• Owning the full stack. Frontend, backend, data, integrations, deployment, the URL you send the stakeholder, and the observability around it once it's live.
• Codifying what works. When you find a pattern that ships across engagements — a prompt structure, an evaluation harness, an integration shim, an agent template — you push it back to the platform and engineering teams so the next FDE doesn't rebuild it.
• Owning the relationship over time. The first prototype is the start, not the deliverable. You stay close to the line of business through the lifecycle of an engagement, identify new opportunities as they surface, and harden what graduates into production.

Skills

Full-stack engineering

CS fundamentals

Frontend expertise

Backend development

LLM experience

AI tooling

Data fluency

Cloud deployment

Integration experience

Communication skills

Ambiguity navigation

High agency

High cooperation

Currency on AI

Education

Bachelor’s degree in Computer Science

Tools

AWS

GCP

Azure

CI/CD

Containers

Job description

Pearson's PSG-CP team seeks a forward deployed engineer who sits with customers and writes code that ships. You will own end-to-end systems, from discovery to production, in fast-moving engagements. Expect deep frontend and backend work, strong CS fundamentals, and extensive AI/LLM usage across real workflows.

You’ll embed with internal business lines, operate autonomously, and drive AI adoption at scale. This is a high-ownership, high-impact role within Pearson.