AI Engineer

AI Engineer

Position Overview

We are seeking an AI Engineer to join our Global Analytics team in London. This role is focused on the end-to-end lifecycle of production-grade AI from training and fine-tuning specialized models to architecting high-performance inference pipelines.

The ideal candidate views AI as a rigorous engineering discipline. Beyond building models you will be responsible for writing high-quality maintainable Python code and ensuring that every solutionwhether a voice agent or a document processoris built for reliability low latency and global scale.

Key Responsibilities

• Model Training & Fine-Tuning: Lead the adaptation of Large Language Models (LLMs) for domain-specific tasks using techniques likeLoRA QLoRA and PEFT to balance performance with resource efficiency.
• Inference Optimization: Architect and optimize inference pipelines to minimize TTFT (Time to First Token) and maximize throughput. This includes implementing quantization caching strategies and efficient batching.
• Production Engineering: Build and maintain real-time AI pipelines usingWebSockets and SSE ensuring seamless low-latency delivery for voice (ASR/TTS) and text applications.
• Architecture & MLOps: Deploy and orchestrate models within containerized microservice architectures (Docker/Kubernetes) ensuring robust monitoring security and scalability.
• Collaborative Delivery: Work closely with Business Analysts and internal stakeholders to bridge the gap between commercial requirements and technical implementation.

Technical Requirements

• Professional Experience: 5 years in AI/ML engineering with a documented history of moving complex models from research into production.
• Python Mastery: Deep proficiency inPython. You have a strong commitment to clean coding standards (SOLID/DRY) modular design and comprehensive unit/integration testing.
• Generative AI Deep Dive: Hands-on experience with LLM training cycles parameter-efficient fine-tuning (PEFT) and sophisticated prompt engineering.
• Inference Stack: Experience with high-performance inference servers (e.g.vLLM TGI or Triton) and an understanding of how to optimize models for GPU deployment.
• Infrastructure: Comfortable working in Linux-based environments and proficient in managing containerized workloads and automated CI/CD pipelines.
• Advanced RAG: Experience building production-ready Retrieval-Augmented Generation systems including vector database management and semantic search optimization.

Preferred Qualifications

• Experience in the insurance or financial services sector.
• Deep knowledge ofGPU architecture CUDA and hardware-level performance optimization.
• Familiarity with Document Intelligence frameworks (OCR layout analysis and multimodal extraction).