AI Native Software Engineering

Accenture PLC; Greater London; On-site; GBP 75,000 - 120,000; Full time

Job summary

Accenture PLC is seeking an AI Engineer (Software) to design, build, and ship production-grade software across the full stack, using AI-assisted tooling as standard practice. You will work on real client programs, connecting to agentic AI systems and integrating LLM APIs in production code, within an Agile delivery framework.

This role emphasizes contributing to shared AI standards, building robust AI-enabled applications, and advancing your career toward agentic depth and forward-deployed

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or related field.
• Commercial software engineering experience in production environments.
• Proficiency in at least one backend language: Python, Java, or TypeScript.
• Hands-on experience using AI tools in day-to-day engineering work, including LLM API usage in production code.
• Web technologies basics: JavaScript, HTML, CSS.
• Familiarity with cloud fundamentals (AWS/Azure/GCP), Docker, and CI/CD pipelines.
• Understanding of Agile delivery fundamentals.
• Experience with databases (SQL or NoSQL).
• Ability to validate and improve AI-generated outputs; awareness of AI limitations.
• Familiarity with agentic system concepts and orchestration frameworks.

Responsibilities

• Use AI coding assistants daily as a standard part of delivery with measurable impact.
• Integrate LLM APIs into production applications and manage latency/cost tradeoffs.
• Apply AI across the full software delivery lifecycle, including AI-generated tests and debugging.
• Own the quality of AI-generated outputs within your delivery scope and assess production readiness.
• Define and track KPIs for AI-assisted workflows and present results to stakeholders.
• Own delivery end-to-end in Agile sprint cycles with client engineering teams.
• Contribute to shared knowledge bases, reusable components, and internal AI tooling standards.
• Build and integrate application layers, APIs, and interfaces connecting full-stack to AI backends.

Skills

Backend languages

AI in production

Agile delivery

AI tooling usage

Education

Bachelor's degree in CS/CE/SE

Tools

Docker

LLM API integration

AWS

Azure

GCP

Job description

Role Description

We are building the next generation of AI-native engineering talent engineers who use AI as a core part of how they work, not as an add-on. As an AI Engineer (Software), you will design, build, and ship production-grade software across the full stack, using AI-assisted tooling as standard daily practice alongside your core engineering skills.

You will work on real client programs across industries, building production-grade software that connects to and supports agentic AI systems — understanding how your full-stack work integrates with agent architecture, LLM APIs, and enterprise AI pipelines. This is not a stepping-stone role: it is a core engineering function in the most in-demand part of the market, with a direct pathway to the Forward Deployed Engineer program for those who develop agentic depth.

We offer what no single product company can: breadth across every industry, every enterprise technology stack, and every level of organizational complexity — combined with vendor fellowship access inside Anthropic, OpenAI, Microsoft, and Google engineering teams, structured AI certification pathways, and a clear development track toward agentic and forward-deployed engineering.

Key Responsibilities

• Use AI coding assistants daily as a standard part of delivery, actively, frequently, and with demonstrable impact on productivity and output quality
• Integrate LLM APIs into applications in production: calling AI provider APIs in live code, managing token limits and latency, and building initial abstraction layers
• Apply AI across the full software delivery lifecycle: AI-generated tests, AI-assisted debugging, AI-accelerated code review, and prompt engineering for development tasks
• Own the quality of AI-generated outputs in your delivery scope, exercise engineering judgment about reliability, limitations, and failure modes; know when AI output is production-ready and when it is not
• Define and track KPIs to evaluate the effectiveness and ROI of AI-assisted workflows; present AI productivity and quality metrics to project stakeholders
• Own delivery end-to-end — from design through to production support — in Agile sprint cycles alongside client engineering teams
• Contribute to shared knowledge bases, reusable components, and internal AI tooling standards that benefit the wider team
• Build and integrate the application layers, APIs, and interfaces that connect full-stack systems to agentic backends — understanding data flows, context handoffs, and integration points between your code and AI pipelines

Basic Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or a related field
• Commercial software engineering experience in production environments (or equivalent demonstrated through academic projects, internships, or shipped personal projects)
• Proficiency in at least one primary backend language: Python, Java, or TypeScript
• Demonstrated hands-on experience using AI tools actively in day-to-day engineering work — with practical examples of how AI was used to solve real problems, iterate on outputs, and improve delivery; including direct experience calling LLM APIs in production code with an understanding of token management, latency, and cost tradeoffs
• Basic understanding of web technologies including JavaScript, HTML, and CSS
• Familiarity with cloud fundamentals (AWS, Azure, or GCP), containers (Docker), and CI/CD pipelines
• Understanding of Agile delivery fundamentals
• Experience with databases — SQL or NoSQL
• Ability to validate, evaluate, and improve AI-generated outputs; understanding of AI limitations and responsible use
• Familiarity with agentic system concepts — awareness of orchestration frameworks (LangChain, LangGraph, or equivalent), RAG pipelines, and how full-stack applications connect to agent-based architecture; production experience preferred, conceptual understanding required