4 of 4 Remote/Hybrid Artificial Intelligence Engineering Jobs in the North East

contributing to our internal communities and best practices.* Capability Development - Contribute to thought leadership, blog posts, or internal accelerator development in emerging AI engineering topics such as Agentic AI, LLMOps, or evaluation frameworks.* Experience working in a major Consulting firm, and/or in industry but having ...

writing high‐quality, tested, maintainable code and staying close to implementation rather than delegating it away. Technical Leadership Within the Squad Lead the engineering practice within your delivery team: set coding standards, review designs and pull requests, unblock technically complex problems, and mentor other engineers day to day. … Practitioner) — delivering iteratively within a blended, multidisciplinary team including civil servants. Given the scale of this programme, direct hands‐on depth across the AI engineering stack is essential: Strong general‐purpose programming (most commonly Python) applied to AI/ML systems Semantic search, vector/embedding infrastructure ...

writing high-quality, tested, maintainable code and staying close to implementation rather than delegating it away. Technical Leadership Within the Squad Lead the engineering practice within your delivery team: set coding standards, review designs and pull requests, unblock technically complex problems, and mentor other engineers day to day. … Practitioner) - delivering iteratively within a blended, multidisciplinary team including civil servants. Given the scale of this programme, direct hands-on depth across the AI engineering stack is essential: - Strong general-purpose programming (most commonly Python) applied to AI/ML systems - Semantic search, vector/embedding infrastructure ...

checks to strengthen the multidisciplinary delivery team. Alongside the DDaT competencies, given the scale of this programme, hands-on depth across the modern AI engineering stack is essential, not just RAG in isolation: Semantic search, vector/embedding infrastructure, and retrieval architecture design LLM orchestration and agentic frameworks … deployment pipelines, monitoring, and rollback for AI services in production Fine-tuning and parameter-efficient adaptation techniques where appropriate to use case Data engineering for AI: ingestion, cleaning, and lineage across Legacy and multi-cloud sources Infrastructure-as-Code and CI/CD practices adapted for AI workloads ...