Responsibilities
Deploy machine learning models as reliable, low-latency services consumed by the mobile app backend.
Build and schedule feature pipelines, including daily refreshes of customer-level scores.
Integrate external market-news feeds and product-analytics data into the data warehouse.
Implement model monitoring, drift detection, alerting, and retraining workflows.
Establish ML deployment patterns, CI/CD, infrastructure-as-code, and testing standards in production.
Document systems for long-term ownership and deliver a handover plan.
Requirements
At least 4 years of software or ML engineering experience with models running in production.
Strong Python skills and experience serving models through REST or gRPC APIs, Docker, and Kubernetes or equivalent.
AWS experience, including S3, compute, and managed ML services.
Experience building data pipelines and orchestration with Airflow or similar tools, including third-party API integration.
Experience with CI/CD, infrastructure-as-code,



observability tooling, and high-availability environments where outages affect customers.
Financial services, brokerage, regulated-industry, feature-store, ML-platform, market-data, or financial-news-feed experience is preferred.
Benefits
Competitive salary package, share plan, company performance bonuses, value-based recognition awards, and referral bonus.
Career coaching, global career opportunities, internal development programs, and technical and management leadership paths.
Training, certifications, coaching, online learning subscriptions, workshops, conferences, and other learning opportunities.
Hybrid work and versátil working hours.
Employee assistance and global wellbeing programs, including access to wellbeing apps.
Internal technology communities, hobby clubs, inclusion and diversity programs, events, and celebrations.

📌 Machine Learning Engineer (Buenos Aires)
🏢 Endava
📍 Buenos Aires