MLOps/AI Engineer/ Data Scientist

Capco Poland is a global technology and management consultancy partnering with leading organizations across banking, payments, capital markets, wealth, and asset management. The company focuses on shaping the future of financial services through technology, data, and AI, providing solutions for complex challenges in highly regulated industries.

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 into real-world applications.

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.

General:

• 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 a 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.

Nice to have:

• 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.

• 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 clients.