Architecture internship Work, Jobs in Newcastle Upon Tyne, Eng (with Salaries)

Junior AI Software Engineer
Accenture —Newcastle upon Tyne
- Full-time
- Computer Science
- Engineering
- Master's degree
Software Engineering Team Lead
M-KOPA —Newcastle upon Tyne
- Full-time
- Remote
- DevOps
5d

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Engineering jobs in Newcastle Upon Tyne

Junior AI Software Engineer

Accenture -
Newcastle upon Tyne

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Job details

Full-time

Qualifications

Law
Computer Science
Engineering
NoSQL
Master's degree
Databases
Docker
Bachelor's degree
CI/CD pipelines
Back-end development
APIs
Agile
AI
Debugging

Full 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 solutions — understanding how your full-stack work integrates with agent architecture, LLM APIs, and enterprise AI pipelines. This is not a stepping-stone 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

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

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