AI Engineers (LLM)

TL;DR

AI Engineer (LLM): Design, build, and deploy production-grade AI systems powered by large language models with an accent on multi-agent orchestration, intelligent tool integration, and robust production workflows. Focus on crafting evaluation frameworks, implementing feedback loops, and optimizing for latency, cost, and quality across model providers.

Location: London

Company

Global AI-native consultancy trusted by leading enterprises to deliver AI into production at scale across Financial Services, Energy & Utilities, Healthcare & Life Sciences, Retail & CPG, and Manufacturing.

What you will do

• Design and implement production AI systems integrating LLMs, RAG pipelines, vector databases, and agentic frameworks.
• Create evaluation frameworks to measure and monitor system performance, accuracy, and reliability.
• Build and maintain production-grade AI applications with clean code, error handling, APIs, and data pipelines.
• Implement retrieval systems including vector/graph databases, ingestion pipelines, and techniques like HyDE.
• Develop feedback loops and observability for continuous system improvement.
• Craft effective prompts and optimize for latency, cost, and quality.

Requirements

• Hands-on experience building applications with LLM APIs and deep understanding of capabilities, limitations, and failure modes.
• Practical implementation of RAG architectures, vector databases, knowledge graphs, and prompt engineering.
• Experience building multi-step LLM workflows and agentic systems using frameworks like LangGraph or custom implementations.
• Strong Python proficiency with production API/service development and cloud platforms (AWS, GCP, Azure).
• Understanding of distributed systems, CI/CD, testing, deployment pipelines, and cloud-native infrastructure.
• Strong data manipulation skills (pandas, SQL) and evaluation strategies for LLM-based systems.
• Ability to work with ambiguity, experiment, and balance latency/cost/quality tradeoffs.

Nice to have

• Experience with AI-assisted coding tools like Claude Code, OpenAI Codex, GitHub Copilot.
• Fine-tuning LLMs for domain-specific applications.
• Real-time streaming, multimodal models, or search technologies like Elasticsearch.
• Model observability tools (LangSmith, Weights & Biases) and cost optimization.
• Experience in verticals like financial services, energy, healthcare with compliance and security practices.
• Setting up tool calling agents, handoffs, and guardrails.

Culture & Benefits

• Fast-growing startup with career growth opportunities.
• Competitive salary and company bonus.
• Collaborative environment where every viewpoint is considered.
• Financially backed for security and global expansion.
• Pick your own gear (MacBooks, PCs, accessories).
• Personal learning budget for development.