Our client, a
Financial Services organisation
, is seeking an experienced
AI Engineer
to join a growing AI and data engineering function. This role focuses on building and operating production-grade AI systems tailored for financial use cases, leveraging large-scale data from multiple financial and media sources.
The successful candidate will work on advanced AI engineering initiatives, including LLMs, multimodal models, and recommendation systems, within a regulated and security-sensitive environment.
Key Responsibilities
Design, fine-tune, and optimise AI models, including
Large Language Models (LLMs)
and multimodal models, to deliver personalised, finance-related content
Build and operate end-to-end AI platforms covering data ingestion, model training, evaluation, optimisation, deployment, and production operations
Develop and maintain custom fine-tuning pipelines aligned with
financial regulatory and security requirements
(e.g. KYC, AML, data governance)
Design and implement personalised recommendation systems using advanced techniques such as
graph-based recommendation models
, informed by user behaviour patterns
Build real-time feature pipelines using large-scale user interaction and behavioural data
Implement feedback loops to continuously improve model accuracy, relevance, and performance in production
Required Qualifications
3+ years of experience as an
AI / Machine Learning Engineer
in a production environment
Bachelor’s degree or higher in
Computer Science
, Engineering, or a related discipline
Strong hands-on experience with modern deep learning frameworks such as
PyTorch
and
Hugging Face
Proven experience developing, deploying, and operating deep learning models at scale using user behaviour data
Strong experience working with
LLMs or multimodal models
in production settings
Solid background in
feature engineering
and data pipeline development
Experience with model serving and inference platforms such as
Triton
,
TorchServe
, or
BentoML
Preferred Qualifications
Prior experience working in
Financial Services
, fintech, or other regulated, data-intensive environments
Experience designing or operating
LLMOps / MLOps
platforms
Experience with
GPU profiling, performance optimisation, and tuning
Exposure to training and deploying AI models in
hybrid cloud
or multi-environment architectures
Technical leadership experience or ownership of AI-driven initiatives
Hands-on experience with
Kubernetes and Docker
for deployment and operations
Academic publications in AI/ML or contributions to open-source projects