GenAI / MLOps ENGINEER (m/f/d/)
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Summary
At Schwarz Group's IT unit in Sofia, this engineer designs, builds, and owns GenAI/LLM agentic workflows end-to-end — data transformation pipelines, LLM evaluation, and MLOps — then rolls them out across Schwarz's international markets. Core stack: Python, FastAPI, MLflow, CI/CD and containerization; bonus Pydantic AI, GCP, StackIT.
Make an amazing climb in your career in an international team of experts. Our company provides technological services for the whole Schwarz group of more than 30 countries in Europe and the US. Our vision is to be the leading ecosystem for a better life. We built the European sovereign cloud STACKIT. With XM Cyber we set new standards in differing cyber crimes. We run AI better than anyone. With us you will find a variety of opportunities to grow and do your best at your calling – IT. We exist to improve life with our products and services - for today's generation and future generations. We act future proof!
The impact you will create:
- Build Agentic Workflows: Design and deploy scalable, cost-efficient, and highly accurate AI workflows from the ground up.
- Drive Data Transformation: Engineer systems to extract, transform, and classify complex external data in strict alignment with legal frameworks, regulations, and business rules.
- Own the Architecture: Take complete end-to-end ownership of your projects, independently implementing individual workflow steps or entire automated systems with a relentless focus on architectural excellence and clean code.
- Lead Global Rollouts: Autonomously scale and deploy these GenAI solutions to new countries and markets.
Partner with Stakeholders: Act as the technical bridge, driving communication with stakeholders to translate complex business needs into precise technical requirements.
Experience and skills you will need:
- Core Software Engineering: Strong proficiency in Python and modern engineering practices, specifically async programming, FastAPI, and clean code principles.
- GenAI Expertise: Proven, hands-on experience building, optimizing, and deploying generative AI and LLM applications.
- Eval-Driven Development: A solid grasp of data science fundamentals applied to AI, focusing on systematic LLM evaluation, dataset curation, and performance metrics.
- DevOps & Infrastructure: Deep understanding of CI/CD pipelines, containerization, deployment strategies, and secure secret management.
- LLMOps & Observability: Practical experience using tools like MLflow to monitor, track, and maintain system health in production.
- Enabler Mindset: A proactive approach to mentoring peers, sharing knowledge, and establishing robust engineering standards across the team.
- Bonus Qualifications (Desirable): Prior experience working with Pydantic AI, Google Cloud Platform (GCP), and StackIT.