Machine Learning Engineer - Fintech – Remote
Machine Learning Engineerwanted 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.
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