MLOps/AI Engineer/ Data Scientist

CAPCO POLAND

DATA SCIENTIST / AI ENGINEER / MLOPS ENGINEER

AI INFUSED. THE FUTURE IS BUILT, NOT JUST IMAGINED.

Location: Kraków, Poland

WHY JOIN CAPCO?

You’ll join an environment whereAI 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.

You’ll have the opportunity toexperiment, 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 aData Scientist, AI Engineer, MLOps Engineer — or somewhere at the intersection of all three — we want to hear from you.

ABOUT US

AtCapco Poland, we’re not just another consultancy — we’re helping shape the future of financial services through technology, data, and AI.

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

Our culture isfast-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, we’re looking for talented professionals acrossData Science, AI Engineering, and MLOps who want to design, build, and operationalize the next generation of intelligent solutions.

You don’t need to fit neatly into one box. Whether your strengths lie indeveloping models, engineering AI applications, or building the platforms that bring AI into production, we’d like to hear from you.

HOW YOU WILL MAKE MAGIC HAPPEN

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 acrossMachine Learning, Generative AI, Large Language Models (LLMs), and intelligent automation.

• Build robust AI applications and services usingPython, APIs, microservices, and cloud-native technologies.

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

• Design and maintainML 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 supportingproduction-grade AI workloads.

• Apply modernMLOps, DevOps, and software engineering practices to AI solutions.

• Work withDocker, 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 promoteengineering 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.

WHAT MAKES YOU AWESOME

We’re interested in different AI profiles, so we don’t expect every candidate to have experience in everything listed below.

We’re 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 evaluatingmachine 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

• StrongPython and software engineering skills, including clean code, testing, design patterns, and architectural principles.

• Experience building AI-powered applications, services, or platforms.

• Knowledge ofAPI design, microservices, distributed systems, or cloud-native application development.

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

MLOps & AI Platforms

• Experience building or operatingML/AI infrastructure and production pipelines.

• Practical knowledge ofDocker 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.

WHAT WE VALUE ACROSS ALL PROFILES

• Around4+ years of professional experience in software engineering, data science, machine learning, MLOps, AI engineering, or a closely related area.

• A university degree inComputer Science, Mathematics, Physics, Engineering, or another relevant discipline — or equivalent practical experience.

• Strong problem-solving skills and an engineering mindset.

• Understanding of goodsoftware engineering and application design practices.

• Ability to work effectively in anAgile, 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 withinbanking, financial services, or another highly regulated industry is particularly welcome.

GREAT IF YOU ALSO HAVE

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

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

• Experience designingLLM/RAG architectures, vector search, embeddings, AI agents, or GenAI applications.

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

• Experience designing and developingmicroservices architectures.

• Familiarity with establishedMLOps frameworks and ML lifecycle best practices.

• Experience deploying and operating applications or ML workloads onKubernetes.

• Knowledge ofDevOps, Infrastructure as Code, CI/CD, observability, and production monitoring.

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

ONLINE RECRUITMENT PROCESS*

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.