Buscamos un/a Senior Backend Engineer (Ruby on Rails) que combine sólidos fundamentos de ingeniería backend con excelentes habilidades de comunicación, pensamiento analítico y la capacidad de impulsar iniciativas de forma independiente desde el concepto hasta la entrega. Trabajarás en estrecha colaboración con equipos de ingeniería distribuidos en América Latina y Estados Unidos, contribuyendo al diseño de sistemas, decisiones arquitectónicas y la mejora continua de sistemas complejos.
Locations (4)
Panamá, Panama
Nicaragua, Nicaragua
Málaga, Andalusia, Spain
Madrid, Madrid, Spain
Responsibilities
Diseñar, desarrollar y mantener servicios backend escalables y características críticas para el negocio.
Impulsar de forma independiente iniciativas técnicas desde la definición inicial del problema hasta la implementación y entrega.
Navegar por sistemas heredados complejos, comprender dependencias y solucionar problemas técnicos desafiantes.
Participar en el diseño de sistemas y discusiones arquitectónicas, evaluando compensaciones y proponiendo soluciones efectivas.
Colaborar con ingenieros y partes interesadas de múltiples equipos para alinear prioridades e impulsar los resultados del proyecto.
A hands-on Engineering Manager to lead a team of around 10 engineers on a long-running and successful project, working with an East Coast-based US client, spending 75% of time coding as a senior Java engineer and 25% leading the team.
Compensation
$7,000 – $8,500 per month for B2B engagement, based on experience and skills
Responsibilities
Build and maintain backend applications and microservices using Java, Spring, and Spring Boot
Own technical decisions across architecture and system design
Write production-ready code, participate in code reviews, and maintain engineering quality
Debug and improve performance, reliability, and scalability
Coordinate planning, prioritization, delivery, and unblocking for a team of around 10 engineers
Join the Risk & Control Governance team / Risk Assessment Group to build the governance, assurance, and data foundations that ensure every risk decision at Wise is consistent, explainable, auditable, and continuously monitored.
Responsibilities
Build Data Trust: Implement data quality monitoring, data contracts, and lineage tracking that give the organisation full traceability from risk decisions back to source data.
Deliver Continuous Assurance: Design and build automated monitoring systems that continuously validate control effectiveness — detecting drift, false positives/negatives, and coverage gaps before they become incidents.
Enable Auditability: Create audit trail infrastructure and explainability tooling that dramatically reduces the effort required for regulatory reporting and compliance evidence generation.
Integrate with Risk API: Work closely with the Risk API team to embed governance and assurance capabilities into the core platform, ensuring clean integration points and shared data contracts.
Lead and grow a team of machine learning, backend, and data engineers, shaping technical strategy and execution for personalized listening experiences.
Locations (2)
Stockholm, Stockholm County, Sweden
London, England, United Kingdom
Responsibilities
Lead and grow a team of machine learning, backend, and data engineers
Shape the team's technical strategy and execution
Guide the development of machine learning and recommendation systems
Collaborate with Product and engineering partners to define priorities
Support the team in making thoughtful technical decisions across experimentation, scalability, system reliability, and model performance
Own the data that Falcon’s offer-ranking model learns from: the raw sources, the pipelines, the training tables, and the monitoring around them.
Responsibilities
Own the ML data pipelines: build and run batch pipelines on Spark and Databricks, from raw data to feature and training tables, including enrichment and backfills for late-arriving data.
Onboard new data sources: bring in internal databases, external APIs, partner feeds and web data, and match them to Falcon’s own entities.
Use LLMs inside pipelines: add LLM steps to batch jobs, such as turning text into embeddings and structured attributes.
Keep training and serving consistent: make sure features match between training and the live inference service, which includes reading its Java code.
Monitor data and model inputs: run scheduled data quality checks and Grafana alerts that catch bad data before it reaches the model, and find the root cause when something breaks.
Senior Backend Engineer (Ruby on Rails)NewCoderland · Remote, Panama+3 more
Staff Software Engineer - BackendNewSurveyMonkey · Bengaluru, India
Staff Backend Engineer - Operations Tech TeamNewiwoca · London, United Kingdom+3 more
Machine Learning Engineering Manager IINewSpotify · Stockholm, Sweden+1 more
Senior MLOps Engineer (Data & ML Platform)NewSD Solutions · Georgia
Senior Backend Engineer - TransactionNewEther.fi · New York, US