AI Native Software Engineering

Accenture; Greater London; On-site; GBP 70,000 - 120,000; Full time

Job summary

Accenture is seeking an AI Engineer (Software) to design, build, and ship production-grade software across the full stack, using AI-assisted tooling as standard daily practice alongside core engineering skills.

You will work on real client programs across industries, connecting to agentic AI systems, LLM APIs, and enterprise AI pipelines, with a clear path toward advanced engineering roles and certification pathways.

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or 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.
• Hands-on experience using AI tools actively in day-to-day engineering work — including direct experience calling LLM APIs in production code with understanding of token management, latency, and cost tradeoffs.
• 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.
• Familiarity with agentic system concepts — LangChain, LangGraph, RAG pipelines, etc.

Responsibilities

• Use AI coding assistants daily as a standard part of delivery, 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; assess reliability and production readiness.
• Define and track KPIs to evaluate AI-assisted workflows; present AI productivity and quality metrics to stakeholders.
• Own delivery end-to-end — from design through production support — in Agile sprint cycles with client teams.
• Contribute to shared knowledge bases, reusable components, and internal AI tooling standards.
• Build and integrate application layers, APIs, and interfaces that connect full-stack systems to agentic backends.

Skills

AI tooling

LLM APIs

Full-stack development

Agile delivery

Code quality

Prompt engineering

Token management

CI/CD

Testing & debugging

Data integration

Education

Bachelor's degree in CS/Engineering

Tools

Python

Java

TypeScript

Docker

Job 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
• 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