AI Engineer - Legal Tech & LLM Systems

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

Benefits offered by this job

Competitive salary & equity

Autonomy & ownership

Learning & development budget

Bi-annual team retreats

Job summary

Wexler is building the leading gen-AI platform for litigation, headquartered in London. We seek an exceptional AI engineer to shape the future of legal work and oversee the AI strategy, crafting agentic systems that analyse millions of documents to extract critical factual information for winning cases.

You will design LLM agents, develop robust evaluation pipelines, and drive prompt engineering while balancing token limits, cost and latency.

Qualifications

• Expert-level Python with a track record of shipping production LLM systems.
• Hands-on experience designing LLM agents and RAG pipelines.
• Proficiency with AI evaluation frameworks and end-to-end evaluation pipelines.
• Solid grounding in NLP fundamentals (tokenisation, embeddings, bias handling).

Responsibilities

• Design, build and refine production-grade LLM agents and proprietary algorithms for expert litigators.
• Lead development of robust evaluation frameworks and pipelines, establishing metrics and benchmarking models on large datasets to steer R&D.
• Drive advanced prompt-engineering practice to maximise model efficacy.
• Apply core NLP fundamentals (tokenisation, embeddings, model architecture) while keeping outputs structured, performant and cost-efficient.
• Own clean, modular Python back-end code for data-intensive systems, balancing real-world constraints such as token limits, cost and latency.

Skills

Python

LLM development

NLP fundamentals

Prompt engineering

AI evaluation

Tools

RAG pipelines

Production back-end

Job description

Wexler is building the leading gen-AI platform for litigation, headquartered in London. We seek an exceptional AI engineer to shape the future of legal work and oversee the AI strategy, crafting agentic systems that analyse millions of documents to extract critical factual information for winning cases.

You will design LLM agents, develop robust evaluation pipelines, and drive prompt engineering while balancing token limits, cost and latency.