AI Engineer (Fluent in Mandarin & English)
Chubb Open · verified Oct 22, 2026 At a glance
Mid-level AI Engineering role at Chubb. London · full-time · no salary stated · we estimate £77,500–£137,500.
Chubb has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Adapt large language models for specialized tasks, using efficient fine-tuning methods such as LoRA and QLoRA
- Shape inference systems to return first tokens quickly and handle high request volumes through quantization, caching and batching
- Build real-time voice and text pipelines with WebSockets or SSE, keeping delivery responsive
- Deploy models as containerized microservices and maintain their monitoring, security and ability to scale
- Translate commercial needs into technical work with business analysts and internal stakeholders
What you bring
- At least five years in AI or machine-learning engineering, including taking complex models from research into production
- Strong Python skills, with modular code, clean design practices and thorough unit and integration tests
- Practical experience training and fine-tuning LLMs, using PEFT and advanced prompt design
- Experience with production RAG, vector databases and semantic search, plus a high-performance inference server
- Fluency in English and either Mandarin or Cantonese
Who this fits
This mid-level role suits an AI engineer who can take models through production and build reliable, low-latency systems. The position is based in London, and English plus Mandarin or Cantonese fluency is required.
From the employer
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 solution—whether a voice agent or a document processor—is 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 like LoRA, 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 using WebSockets 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 in Python. 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 of GPU architecture, CUDA, and hardware-level performance optimization.
- Familiarity with Document Intelligence frameworks (OCR, layout analysis, and multimodal extraction).
- MUST be fluent in Mandarin OR Cantonese and English
We offer in return!
Competitive salary & pension scheme, discretionary bonus scheme, 25 days annual leave plus ability to purchase 5 additional days, hybrid working options, Private Medical cover, Employee Share Purchase Plan, Life Assurance, Subsidised gym membership, Comprehensive Learning & development offerings, Employee Assistance program.
Integrity. client focus. respect. excellence. teamwork
Our core values dictate how we live and work. We’re an ethical and honest company that’s wholly committed to its clients. A business that’s engaged in mutual trust and respect for its employees and partners. A place where colleagues perform at the highest levels. And a working environment that’s collaborative and supportive.
Diversity & Inclusion. At Chubb, we consider our people our chief competitive advantage and as such we treat colleagues, candidates, clients, and business partners with equality, fairness and respect, regardless of their age, disability, race, religion or belief, gender, sexual orientation, marital status or family circumstances.
We are committed to ensuring our recruitment process is inclusive and accessible to all. If you have a disability or long-term condition (for example dyslexia, anxiety, autism, a mobility condition or hearing loss) and need us to make any reasonable adjustments, changes or do anything differently during the recruitment process, please let us know.