AI Engineer - Legal LLM Systems (London, Equity)

Vimes Inc.; Greater London; Hybrid; GBP 70,000 - 90,000; Full time

Benefits offered by this job

Competitive salary & equity

Autonomy & ownership

Learning budget

Bi-annual team retreats

Job summary

Wexler is building the next generation of AI for litigation in London. We are seeking an exceptional AI engineer to shape the future of legal work by designing production-grade LLM agents and proprietary algorithms for expert litigators.

You will influence the AI strategy as a core team member within a fast-growing startup. You will lead the development of robust evaluation frameworks, drive prompt-engineering practices, and ensure outputs are structured, cost-efficient, and scalable.

Qualifications

• Expert-level Python with production LLM experience.
• Hands-on design of LLM agents and RAG pipelines.
• Proficiency with AI evaluation frameworks and end-to-end evaluation pipelines.
• Strong grounding in NLP fundamentals including tokenisation and embeddings.

Responsibilities

• Design, build and refine production-grade LLM agents and proprietary algorithms for expert litigators.
• Lead development of evaluation frameworks and pipelines, defining metrics and benchmarking models on large datasets.
• Drive advanced prompt-engineering practices to maximise model efficacy.
• Apply NLP fundamentals while keeping outputs structured, performant and cost-efficient.
• Own clean, modular Python back-end code for data-intensive systems considering token limits, cost and latency.

Skills

Python

LLM agents

RAG pipelines

Evaluation frameworks

NLP fundamentals

Prompt engineering

Job description

Wexler is building the next generation of AI for litigation in London. We are seeking an exceptional AI engineer to shape the future of legal work by designing production-grade LLM agents and proprietary algorithms for expert litigators.

You will influence the AI strategy as a core team member within a fast-growing startup. You will lead the development of robust evaluation frameworks, drive prompt-engineering practices, and ensure outputs are structured, cost-efficient, and scalable.