Machine Learning Engineer - Fintech – Remote MachineLearning Engineer wanted as our team is growing fast! Callinghighly motivated, bright candidates who are looking for a career atan exciting award winning FinTech firm! Company: Wealth DynamixRole: Machine Learning Engineer Location: London Start Date: June /July 2025 Would you like to join one of the fastest growing FinTechfirms in Europe? We are looking for an analytical self-starter withexperience in deploying AI ? ML models in the capacity of a DataEngineer. If you are passionate about digital transformation andkeen to learn about delivering the market leading Client LifecycleManaging solution to the Wealth Management industry, apply now! Whoare we? - Wealth Dynamix helps to relieve the burden of clientmanagement issues for wealth management and private banking firmswith innovative technology. - We provide Relationship Managers witha multi-award winning digital Client Lifecycle Management (CLM)platform, offering 360-degree access to their client. - We are aglobal leader in end-to-end CLM, Wealth Dynamix has offices andclients in three continents with headquarters in the UK. What isthe role? This role is geared toward building internal ML toolingcapabilities and bringing LLM/NLP-based features into production,ensuring they are scalable, reliable, and tightly integrated withinour 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 growwhile gaining exposure to innovative tooling in the LLM and MLOpsspace Main Purpose of Role LLM/NLP Production Engineering - Buildand maintain scalable, production-ready pipelines for NaturalLanguage Processing and Large Language Model (LLM) features. -Package and deploy inference services for ML models andprompt-based LLM workflows using containerised services. - Ensurereliable model integration across real-time APIs and batchprocessing systems. Pipeline Automation & MLOps - Use ApacheAirflow (or similar) to orchestrate ETL and ML workflows. -Leverage MLflow or other MLOps tools to manage model lifecycletracking, reproducibility, and deployment. - Create and managerobust CI/CD pipelines tailored for ML use cases Infrastructure& Monitoring - Deploy containerised services using Docker andKubernetes, optimised for cloud deployment (Azure preferred). -Implement model and pipeline monitoring using tools such asPrometheus, Grafana, or Datadog, ensuring performance andobservability. - Collaborate with DevOps to maintain and improveinfrastructure scalability, reliability, and cost-efficiency. -Design, build and maintain internal ML tools to streamline modeldevelopment, training, deployment and monitoring Collaboration& Innovation - Work closely with data scientists toproductionise prototypes into scalable systems. - Participate inarchitectural decisions for LLMOps and NLP-driven components of theplatform. - Stay engaged with the latest developments in modelorchestration, LLMOps, and cloud-native ML infrastructure. - Ensurethe security of systems, data, and people by following companysecurity policies, reporting vulnerabilities, and maintaining asecure work environment across all settings. Why should you apply?- This is a fantastic opportunity to work in a growing FinTechenvironment with excellent career progression available. - With aglobal client base the role offers an opportunity to experience awide variety of digital transformation projects – each with theirown unique requirements and opportunities. - We take careerprogression seriously, with investment into the WDX Academy for newand existing employee learning and development. - You will have theflexibility to work from home, in the office or remotely. Who isbest suited to this role? - 2–3 years of experience in MLengineering or MLOps / LLMOps. - Strong Python programming skillsfor data manipulation and pipeline development. - Hands-onexperience with containerisation using Docker and Kubernetes. -Proven experience deploying ML models into production, ideally inreal-time or SaaS environments. - Familiarity with Airflow, MLflow,and modern MLOps/LLMOps tooling. - Practical experience with cloudplatforms, preferably Microsoft Azure. - Strong problem-solvingskills, attention to detail, and the willingness to get thingsdone. - Excellent collaboration and communication skills;comfortable working across technical and product teams. - PreferredStrengths - Experience with LLMOps frameworks (e.g., LangChain,vector databases, retrieval-augmented generation). - Experiencewith ML-specific CI/CD pipelines and model governance bestpractices. - Familiarity with monitoring and observability toolslike Jaeger, Prometheus, Grafana, or Datadog. - Experience workingin startups or fast-paced teams, balancing rapid iteration withproduction-grade reliability. We believe we offer career definingopportunities and are on a journey that will build awesome memoriesin a diverse and inclusive culture. If you are looking for morethan just a job, get in touch. #J-18808-Ljbffr
