Machine Learning Engineer

We are seeking ML Engineers to take machine learning models into production for our new mobile trading app. They will deploy, operate and monitor the models, and build the data pipelines those models depend on. What they will do Deploy models as reliable, low-latency services consumed by the mobile app backend Build and schedule feature pipelines, including daily refresh of customer-level scores Integrate an external market-news feed (e.g. Refinitiv/LSEG) and product analytics data (Amplitude, in-app video engagement) into our data warehouse Implement model monitoring, drift detection, alerting and retraining workflows Establish deployment patterns, CI/CD and testing standards for ML in our production environment Document everything needed for long-term ownership and deliver a handover plan Must have 4+ years in software or ML engineering, with models running in production Strong Python; model serving via REST/gRPC APIs, Docker and Kubernetes or equivalent AWS experience (e.g. S3, compute, managed ML services) Data pipelines and orchestration (Airflow or similar), including third-party API integration CI/CD, infrastructure-as-code and observability tooling Experience in high-availability environments where outages affect customers Nice to have Financial services, brokerage or other regulated-industry experience Feature stores and ML platforms (e.g. MLflow, SageMaker, Feast) Market data or financial news feeds Discover some of the global benefits that empower our people to become the best version of themselves: Finance: Competitive salary package, share plan, company performance bonuses, value-based recognition awards, referral bonus; Career Development : Career coaching, global career opportunities, non-linear career paths, internal development programmes for management and technical leadership; Learning Opportunities: Complex projects, rotations, internal tech communities, training, certifications, coaching, online learning platforms subscriptions, pass-it-on sessions, workshops, conferences; Work-Life Balance: Hybrid work and flexible working hours, employee assistance programme; Health: Global internal wellbeing programme, access to wellbeing apps; Community: Global internal tech communities, hobby clubs and interest groups, inclusion and diversity programmes, events and celebrations. At Endava, we’re committed to creating an open, inclusive, and respectful environment where everyone feels safe, valued, and empowered to be their best. We welcome applications from people of all backgrounds, experiences, and perspectives—because we know that inclusive teams help us deliver smarter, more innovative solutions for our customers. Hiring decisions are based on merit, skills, qualifications, and potential. If you need adjustments or support during the recruitment process, please let us know.