Artificial Intelligence Engineer

At Dabster, we are your one-stop destination for talent acquisition and digital innovation. Our customized, scalable talent solutions empower organizations to concentrate on their core business while we expertly match the right talent to the right roles.

Who will you work with:

Partnering with a global technology leader that is driving innovation across cloud, data, AI, and enterprise solutions. They offer an exciting environment for professionals who want to contribute to impactful digital transformation projects and work on cutting-edge technology initiatives.

About the Role:

We’re hiring a Forward Deployment Engineer (FDE) to work directly with enterprise clients across the UK on high-impact AI, cloud, and integration programmes.

What You’ll Do:

• Partner with client stakeholders to understand business challenges and translate them into technical solutions.
• Deliver enterprise integrations, microservices, event-driven architectures, and distributed systems.
• Architect and implement cloud-native solutions on Azure, AWS, and/or GCP using containers, Kubernetes, serverless, and multi-cloud patterns.
• Implement DevSecOps, GitOps, MLOps, and LLMOps practices: CI/CD, Infrastructure as Code (Terraform, Ansible), observability, and automated pipelines.
• Design and deploy Generative AI and Agentic AI solutions: Using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, Semantic Kernel, etc.
• Implementing RAG, vector search (Pinecone, Weaviate, Azure AI Search, FAISS, Chroma), prompt engineering, and secure AI patterns.
• Use AI coding agents in daily work (e.g., Claude Code, GitHub Copilot Coding Agent, Cursor, Windsurf, OpenAI Codex/ChatGPT Agent, Amazon Q Developer, Google Gemini Code Assist, Continue.dev) and be able to demonstrate their impact.
• Apply enterprise architecture principles (TOGAF, Zachman, cloud-native design patterns) to modernise platforms and integrations.
• Embed security best practices: Zero Trust, IAM, encryption, compliance, and secure AI implementations.
• Lead PoCs, accelerators, and innovation initiatives, turning emerging tech into scalable, measurable business outcomes.
• Support data engineering needs: ETL/ELT pipelines, data lakes, streaming, and analytics architectures that underpin AI and BI solutions.

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