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Jobs / capco

GenAI/ML Engineer

capcosenior

In short

  • →Ingeniero de ML/GenAI que construye soluciones completas de IA para servicios financieros.
  • →Día a día: pipelines de datos, modelado, RAG, despliegue en producción y trabajo directo con clientes.
  • →Destacado: trabajo en entornos regulados con alta exigencia en explicabilidad y monitoreo.

Fluent in English

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In ~1 minute you get: who interviews you, the likely questions answered from your CV, and your CV tailored to this job. Free, no card.

The questions they'll ask you

1. ¿Cómo has diseñado un pipeline de RAG para un proceso de conocimiento en entorno financiero regulado?

2. ¿Qué técnicas usas para detectar desplazamiento de datos en modelos en producción?

3. Describe un caso en que optimizaste un modelo de ML con restricciones de negocio y explicabilidad.

🔒 +7 more questions

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💵 USD · Remote · No visa

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What they ask for

  • ✓Experiencia sólida en Python y Spark para procesamiento a gran escala.
  • ✓Habilidades en ingeniería de características y modelado estadístico en entornos regulados.
  • ✓Experiencia práctica con LLMs, RAG y flujos agénicos de GenAI.
  • ✓Conocimiento de CI/CD, Kubernetes y Orquestación de pipelines (Airflow).
  • ✓Capacidad para validar modelos, calibrar umbrales y garantizar explicabilidad.
  • ✓Comunicación efectiva con equipos técnicos y no técnicos en ambientes de clientes.

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

PythonSparkKubernetesAirflowCI/CDLLMsRAGprompt engineeringShapley Valuesscikit-learn

Who should you write to at capco?

Your free dossier identifies the people who'd interview you — their background, what they value, and how to reach out so you stand out before applying.

Joining Capco means joining an organisation that is committed to an inclusive working environment where you’re encouraged to #BeYourselfAtWork. We celebrate individuality and recognize that diversity and inclusion, in all forms, is critical to success. It’s important to us that we recruit and develop as diverse a range of talent as we can and we believe that everyone brings something different to the table – so we’d love to know what makes you different. Such differences may mean we need to make changes to our process to allow you the best possible platform to succeed, and we are happy to cater to any reasonable adjustments you may require. You will find the section to let us know of these at the bottom of your application form or you can mention it directly to your recruiter at any stage and they will be happy to help. About Capco Capco is a global technology and business consultancy, focused on the financial services sector. We are passionate about helping our clients succeed in an ever-changing industry. We're looking for a senior software engineer with strong ML and GenAI skills to deliver end-to-end machine learning solutions for our financial services clients. You'll own the full lifecycle — from data pipelines and model development through production deployment and monitoring — working autonomously in complex, regulated environments. This is your chance to make a real difference! YOUR CAPCO DAY • Design and build end-to-end data pipelines and feature engineering workflows on large-scale transactional data in regulated client environments • Train, calibrate, and validate ML models — supervised, unsupervised, and ensemble — including threshold analysis and production-grade explainability • Develop GenAI-powered solutions such as agentic workflows, RAG systems, and LLM-based automation to accelerate knowledge-intensive client processes • Deploy and monitor models in production with drift detection, performance tracking, and multi-environment release management (Dev → Staging → Prod) • Work directly with client stakeholders — from compliance SMEs to engineering teams — delivering documentation, knowledge transfer, and long-term enablement YOUR CAPCO FIT • Strong software engineering foundation: Python, Spark, CI/CD, containerized deployments (Kubernetes), and production-grade pipeline orchestration (Airflow or equivalent) • Data Science depth: feature engineering at scale, statistical modeling, experiment design, model explainability (e.g. Shapley Values), and threshold calibration under business constraints • Hands-on ML experience: tree-based methods, neural networks, ensemble design, hyperparameter optimization, cross-validation, and out-of-time testing • Practical GenAI experience: LLM integration, prompt engineering, RAG architectures, and agentic workflows for automating knowledge-intensive processes • Fluent Ge

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