Are you ready to take on an exciting challenge with a fast-growing FinTech company? Wealth Dynamix is on the lookout for passionate Machine Learning Engineers to join our dynamic team!

We are an award-winning firm located in London, dedicated to revolutionizing client management within the wealth management industry. If you’re an analytical self-starter with a knack for deploying AI and ML models in a Data Engineering capacity, we want to hear from you!

What sets Wealth Dynamix apart?

• We provide cutting-edge technology that alleviates the complexities of client management for wealth management and private banking firms.
• Our multi-award-winning Client Lifecycle Management (CLM) platform offers Relationship Managers comprehensive access to their clients, improving efficiency and service.
• As a global leader in end-to-end CLM, we operate on three continents and are headquartered in the UK.

What will you be doing?

In this role, you’ll focus on enhancing our ML tooling capabilities and taking LLM/NLP-based features to production. You’ll ensure their scalability and reliability within both our on-premise and SaaS platforms.

This position is perfect for someone who excels in data and model pipeline engineering, thrives in collaborative cross-functional teams, and is eager to grow while exploring the latest innovations in LLM and MLOps.

Key Responsibilities:

LLM/NLP Production Engineering

• Develop 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 containerized services.
• Ensure seamless model integration across real-time APIs and batch processing systems.

Pipeline Automation & MLOps

• Utilize Apache Airflow (or similar tools) to orchestrate ETL and ML workflows.
• Use MLflow or similar MLOps tools for managing model lifecycle tracking, reproducibility, and deployment.
• Create and manage resilient CI/CD pipelines tailored for ML use cases.

Infrastructure & Monitoring

• Deploy containerized services with Docker and Kubernetes, optimized for cloud environments (preferably Azure).
• Implement monitoring for models and pipelines using tools like Prometheus, Grafana, or Datadog, ensuring performance and observability.
• Collaborate with DevOps to enhance infrastructure scalability, reliability, and cost-effectiveness.
• Design and maintain internal ML tools to facilitate model development, training, deployment, and monitoring.

Collaboration & Innovation

• Work in tandem with data scientists to transition prototypes into scalable systems.
• Engage in architectural decision-making for LLMOps and NLP-driven elements of our platform.
• Keep abreast of the latest advancements in model orchestration, LLMOps, and cloud-native ML infrastructure.
• Ensure adherence to security policies, report vulnerabilities, and maintain a secure work environment consistently.

Why should you consider joining us?

• Seize the opportunity to work in a thriving FinTech environment with substantial potential for career advancement.
• Engage in diverse digital transformation projects, each with its unique challenges and opportunities due to our global client base.
• Our commitment to your growth is evident through the WDX Academy, which focuses on learning and development for both new and existing employees.
• Enjoy the flexibility of working from home, the office, or a remote location.

Who is an ideal candidate?

• A minimum of 2-3 years of experience in ML engineering or MLOps/LLMOps.
• Strong proficiency in Python for data manipulation and pipeline development.
• Hands-on experience with Docker and Kubernetes for containerization.
• Proven track record of deploying ML models in production, ideally within real-time or SaaS environments.
• Familiarity with Airflow, MLflow, and contemporary MLOps/LLMOps tools.
• Practical cloud platform experience, preferably with Microsoft Azure.
• Strong problem-solving skills, attention to detail, and a proactive work ethic.
• Excellent collaborative and communication capabilities, comfortable functioning across technical and product teams.
• Preferred Skills
• Exposure to LLMOps frameworks (e.g., LangChain, vector databases, retrieval-augmented generation).
• Experience with ML-centric CI/CD pipelines and best practices in model governance.
• Familiarity with monitoring tools like Jaeger, Prometheus, Grafana, or Datadog.
• Experience in startups or fast-paced environments, adept at balancing rapid iteration with production-grade reliability.

At Wealth Dynamix, we believe in offering career-defining opportunities and fostering a diverse and inclusive culture. If you’re searching for more than just a job, we encourage you to connect with us!