Asks for

The ad says“Practical GenAI experience: LLM integration, prompt engineering, RAG architectures, and agentic workflows for automating knowledge-intensive processes”
The ad says“Strong software engineering foundation: Python, Spark, CI/CD, containerized deployments (Kubernetes), and production-grade pipeline orchestration (Airflow or equivalent)”
The ad says“Strong software engineering foundation: Python, Spark, CI/CD, containerized deployments (Kubernetes), and production-grade pipeline orchestration (Airflow or equivalent)”
The ad says“Hands-on ML experience: tree-based methods, neural networks, ensemble design, hyperparameter optimization, cross-validation, and out-of-time testing”
The ad says“Data Science depth: feature engineering at scale, statistical modeling, experiment design, model explainability (e.g. Shapley Values), and threshold calibration under business constraints”
The ad says“Hands-on ML experience: tree-based methods, neural networks, ensemble design, hyperparameter optimization, cross-validation, and out-of-time testing”
The ad says“Strong software engineering foundation: Python, Spark, CI/CD, containerized deployments (Kubernetes), and production-grade pipeline orchestration (Airflow or equivalent)”
The ad says“Practical GenAI experience: LLM integration, prompt engineering, RAG architectures, and agentic workflows for automating knowledge-intensive processes”
The ad says“Data Science depth: feature engineering at scale, statistical modeling, experiment design, model explainability (e.g. Shapley Values), and threshold calibration under business constraints”
The ad says“Hands-on ML experience: tree-based methods, neural networks, ensemble design, hyperparameter optimization, cross-validation, and out-of-time testing”
The ad says“Practical GenAI experience: LLM integration, prompt engineering, RAG architectures, and agentic workflows for automating knowledge-intensive processes”
The ad says“Strong software engineering foundation: Python, Spark, CI/CD, containerized deployments (Kubernetes), and production-grade pipeline orchestration (Airflow or equivalent)”
The ad says“Practical GenAI experience: LLM integration, prompt engineering, RAG architectures, and agentic workflows for automating knowledge-intensive processes”
The ad says“Strong software engineering foundation: Python, Spark, CI/CD, containerized deployments (Kubernetes), and production-grade pipeline orchestration (Airflow or equivalent)”
The ad says“Data Science depth: feature engineering at scale, statistical modeling, experiment design, model explainability (e.g. Shapley Values), and threshold calibration under business constraints”
The ad says“Hands-on ML experience: tree-based methods, neural networks, ensemble design, hyperparameter optimization, cross-validation, and out-of-time testing”

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 German and strong client-facing communication skills, with the ability to produce audit-ready documentation for regulated environments

Ready to take the Next Step

If this sounds like you, we would love to hear from you. This is an opportunity to make a difference and contribute to a highly successful company with a significant growth trajectory.