IntelStack
AI Engineer
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AI Engineer
Job Type: Contract · Inside IR35
Location: London — Hybrid (3 days a week)
Duration: 12 months
The Role
We are seeking a highly skilled AI Engineer with hands-on experience in building AI-powered software solutions using GitHub Copilot, Claude Code, and modern Large Language Models (LLMs). The ideal candidate will leverage AI-assisted development tools to accelerate software delivery, automate engineering workflows, and build intelligent applications utilizing agentic AI frameworks, code generation, and enterprise AI platforms.
The candidate should possess strong software engineering fundamentals along with expertise in integrating AI models into real-world applications and driving developer productivity through AI-assisted coding solutions.
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Your Responsibilities
- Design, develop, and deploy AI-powered applications using LLMs and Generative AI technologies.
- Build engineering solutions leveraging GitHub Copilot, Claude Code, OpenAI, Azure OpenAI, Anthropic Claude, and related AI platforms.
- Develop AI agents, copilots, and workflow automation solutions for enterprise use cases.
- Create retrieval-augmented generation (RAG) solutions using vector databases and knowledge repositories.
- Design and implement prompt engineering frameworks and evaluation mechanisms.
- Integrate AI services with enterprise applications using APIs, microservices, and cloud-native architectures.
- Build automated coding assistants, developer productivity tools, and intelligent software engineering workflows.
- Collaborate with business and technical stakeholders to identify AI-driven opportunities and translate them into scalable solutions.
- Establish best practices for AI governance, security, model monitoring, and responsible AI implementation.
- Mentor development teams on effective usage of AI-assisted development tools.
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Your Profile
Essential skills/knowledge/experience:
- AI & Generative AI
- Strong experience with Large Language Models (LLMs) and Generative AI.
Desirable skills/knowledge/experience:
- Azure, AWS, or Google Cloud Platform.
- CI/CD pipelines using GitHub Actions, Azure DevOps, or Jenkins.
- Docker and Kubernetes.
- Infrastructure as Code (Terraform preferred).
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