About the Role
Our team is growing fast and we are looking for a highly motivated, bright Machine Learning Engineer to join our award-winning FinTech firm. 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, data engineering, or MLOps/LLMOps roles.
- 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.