MLOpsAI Engineer Data Scientist

AI INFUSED. THE FUTURE IS BUILT NOT JUST IMAGINED.

Location: Kraków Poland

Youll join an environment where AI meets real-world transformation.

At Capco you can work with talented people across engineering data architecture and financial services while solving challenging problems for leading organizations.

Youll have the opportunity to experiment build scale and influence how AI is applied in practice whether your passion is creating intelligent models engineering AI products or building the platforms that make AI reliable at scale.

If you see yourself as a Data Scientist AI Engineer MLOps Engineer or somewhere at the intersection of all three we want to hear from you.

At Capco Poland were not just another consultancy were helping shape the future of financial services through technology data and AI.

As a global technology and management consultancy we partner with leading organizations across banking payments capital markets wealth and asset management helping them solve complex challenges and turn ambitious ideas into real-world solutions.

Our culture is fast-moving flexible collaborative and entrepreneurial. We encourage people to challenge the status quo experiment with new technologies and take ownership of what they build.

As we continue to scale our AI capabilities were looking for talented professionals across Data Science AI Engineering and MLOps who want to design build and operationalize the next generation of intelligent solutions.

You dont need to fit neatly into one box. Whether your strengths lie in developing models engineering AI applications or building the platforms that bring AI into production wed like to hear from you.

Depending on your experience and area of expertise you will have the opportunity to:

• Design develop and productionize innovative AI and machine learning solutions addressing real business challenges.

• Explore and implement modern approaches across Machine Learning Generative AI Large Language Models (LLMs) and intelligent automation.

• Build robust AI applications and services using Python APIs microservices and cloud-native technologies.

• Develop reusable AI/ML libraries frameworks components and common assets that accelerate AI adoption at scale.

• Design and maintain ML and AI pipelines supporting the full lifecycle from experimentation and training to deployment monitoring and continuous improvement.

• Build scalable resilient secure and maintainable systems capable of supporting production-grade AI workloads.

• Apply modern MLOps DevOps and software engineering practices to AI solutions.

• Work with Docker Kubernetes cloud platforms CI/CD pipelines model registries monitoring and automation tooling.

• Collaborate with engineers data scientists architects business stakeholders and client teams to turn ideas and prototypes into reliable solutions.

• Help define and promote engineering standards architectural principles reusable patterns and best practices for AI development.

• Stay close to emerging AI technologies and evaluate where they can create meaningful value for our clients.

Were interested in different AI profiles so we dont expect every candidate to have experience in everything listed below.

Were looking for people who bring a strong combination of skills across one or more of these areas:

Data Science & Machine Learning

• Practical experience developing and evaluating machine learning statistical or AI models.

• Strong Python skills and experience with the modern data science and ML ecosystem.

• Understanding of model development experimentation feature engineering validation and performance evaluation.

• Experience taking ML solutions beyond experimentation and into real-world applications is highly valued.

AI Engineering

• Strong Python and software engineering skills including clean code testing design patterns and architectural principles.

• Experience building AI-powered applications services or platforms.

• Knowledge of API design microservices distributed systems or cloud-native application development.

• Hands-on exposure to Generative AI LLMs RAG agents or related AI architectures and frameworks is a strong advantage.

MLOps & AI Platforms

• Experience building or operating ML/AI infrastructure and production pipelines.

• Practical knowledge of Docker and Kubernetes.

• Experience with CI/CD automation model deployment monitoring observability or model lifecycle management.

• Understanding of scalable reliable and secure production environments for ML and AI workloads.

• Around 4 years of professional experience in software engineering data science machine learning MLOps AI engineering or a closely related area.

• A university degree in Computer Science Mathematics Physics Engineering or another relevant discipline or equivalent practical experience.

• Strong problem-solving skills and an engineering mindset.

• Understanding of good software engineering and application design practices.

• Ability to work effectively in an Agile collaborative environment.

• Curiosity and a strong desire to keep learning as AI technologies and engineering practices evolve.

• Ability to communicate technical ideas clearly and collaborate with both technical and non-technical stakeholders.

Experience within banking financial services or another highly regulated industry is particularly welcome.

Any of the following would be an advantage but they are not required for every profile:

• Hands-on experience with frameworks and platforms such as LangChain Haystack Kubeflow or comparable technologies.

• Experience designing LLM/RAG architectures vector search embeddings AI agents or GenAI applications.

• Experience with one or more major cloud platforms and cloud-native AI/ML services.

• Experience designing and developing microservices architectures.

• Familiarity with established MLOps frameworks and ML lifecycle best practices.

• Experience deploying and operating applications or ML workloads on Kubernetes.

• Knowledge of DevOps Infrastructure as Code CI/CD observability and production monitoring.

• Experience working with enterprise-scale data and AI environments.

Screening call with the Recruiter Capco Hiring Manager Interview Client Interview Feedback / Offer

The exact recruitment process may vary depending on the role and project.