Machine Learning Engineer - Fintech – Remote Machine
Learning Engineer wanted as our team is growing fast! Calling
highly motivated, bright candidates who are looking for a career at
an exciting award winning FinTech firm! Company: Wealth Dynamix
Role: Machine Learning Engineer Location: London Start Date: June /
July 2025 Would you like to join one of the fastest growing FinTech
firms in Europe? We are looking for an analytical self-starter with
experience in deploying AI ? ML models in the capacity of a Data
Engineer. If you are passionate about digital transformation and
keen to learn about delivering the market leading Client Lifecycle
Managing solution to the Wealth Management industry, apply now! Who
are we? - Wealth Dynamix helps to relieve the burden of client
management issues for wealth management and private banking firms
with innovative technology. - We provide Relationship Managers with
a multi-award winning digital Client Lifecycle Management (CLM)
platform, offering 360-degree access to their client. - We are a
global leader in end-to-end CLM, Wealth Dynamix has offices and
clients in three continents with headquarters in the UK. What is
the role? This role is geared toward building internal ML tooling
capabilities and bringing LLM/NLP-based features into production,
ensuring they are scalable, reliable, and tightly integrated within
our on premise and SaaS platform. This is a deployment-first role,
for someone who excels at data and model pipeline engineering,
thrives in a collaborative cross-functional team, and wants to grow
while gaining exposure to innovative tooling in the LLM and MLOps
space Main Purpose of Role LLM/NLP Production Engineering - Build
and maintain scalable, production-ready pipelines for Natural
Language Processing and Large Language Model (LLM) features. -
Package and deploy inference services for ML models and
prompt-based LLM workflows using containerised services. - Ensure
reliable model integration across real-time APIs and batch
processing systems. Pipeline Automation & MLOps - Use Apache
Airflow (or similar) to orchestrate ETL and ML workflows. -
Leverage MLflow or other MLOps tools to manage model lifecycle
tracking, reproducibility, and deployment. - Create and manage
robust CI/CD pipelines tailored for ML use cases Infrastructure
& Monitoring - Deploy containerised services using Docker and
Kubernetes, optimised for cloud deployment (Azure preferred). -
Implement model and pipeline monitoring using tools such as
Prometheus, Grafana, or Datadog, ensuring performance and
observability. - Collaborate with DevOps to maintain and improve
infrastructure scalability, reliability, and cost-efficiency. -
Design, build and maintain internal ML tools to streamline model
development, training, deployment and monitoring Collaboration
& Innovation - Work closely with data scientists to
productionise prototypes into scalable systems. - Participate in
architectural decisions for LLMOps and NLP-driven components of the
platform. - Stay engaged with the latest developments in model
orchestration, LLMOps, and cloud-native ML infrastructure. - Ensure
the security of systems, data, and people by following company
security policies, reporting vulnerabilities, and maintaining a
secure work environment across all settings. Why should you apply?
- This is a fantastic opportunity to work in a growing FinTech
environment with excellent career progression available. - With a
global client base the role offers an opportunity to experience a
wide variety of digital transformation projects – each with their
own unique requirements and opportunities. - We take career
progression seriously, with investment into the WDX Academy for new
and existing employee learning and development. - You will have the
flexibility to work from home, in the office or remotely. Who is
best suited to this role? - 2–3 years of experience in ML
engineering or MLOps / LLMOps. - Strong Python programming skills
for data manipulation and pipeline development. - Hands-on
experience with containerisation using Docker and Kubernetes. -
Proven experience deploying ML models into production, ideally in
real-time or SaaS environments. - Familiarity with Airflow, MLflow,
and modern MLOps/LLMOps tooling. - Practical experience with cloud
platforms, preferably Microsoft Azure. - Strong problem-solving
skills, attention to detail, and the willingness to get things
done. - Excellent collaboration and communication skills;
comfortable working across technical and product teams. - Preferred
Strengths - Experience with LLMOps frameworks (e.g., LangChain,
vector databases, retrieval-augmented generation). - Experience
with ML-specific CI/CD pipelines and model governance best
practices. - Familiarity with monitoring and observability tools
like Jaeger, Prometheus, Grafana, or Datadog. - Experience working
in startups or fast-paced teams, balancing rapid iteration with
production-grade reliability. We believe we offer career defining
opportunities and are on a journey that will build awesome memories
in a diverse and inclusive culture. If you are looking for more
than just a job, get in touch. #J-18808-Ljbffr
