Senior MLOps Engineer

Salary Range: 65000 to 80000 (Currency: EUR) (Pay period: per-year-salary)

Responsibilities:

• Develop and maintain scalable MLOps pipelines for GenAI applications.
• Deploy and optimize GenAI models, including large language models (LLMs) such as GPT and similar architectures, in production environments.
• Develop solutions leveraging traditional AI techniques such as decision trees, clustering, and regression analysis to complement advanced AI workflows
• Implement and manage CI/CD pipelines for ML workflows, including testing, validation, and deployment.
• Optimize cloud infrastructure for cost-efficient training and serving of GenAI and LLM models.
• Define and enforce best practices for model versioning, reproducibility, and governance.
• Monitor and troubleshoot production systems to minimize downtime.
• Utilize Databricks to build and manage data and ML pipelines integrated with GenAI and LLM workflows.
• Evaluate and integrate state-of-the-art MLOps tools and frameworks for LLMs and other GenAI models.
• Stay updated on advancements in GenAI technologies, including LLM fine-tuning and serving, and contribute to strategic initiatives.

Qualifications:

• Bachelor's or Master’s degree in Computer Science, Engineering, or a related field.
• 5+ years of experience in MLOps, DevOps, or related roles, focusing on ML and AI.
• Proven expertise in deploying and managing Generative AI models (e.g., GPT, Stable Diffusion, BERT).
• Proficient in Python and ML libraries such as TensorFlow, PyTorch, or Hugging Face.
• Skilled in cloud platforms (AWS, GCP, Azure) and managed AI/ML services.
• Hands-on experience with Docker, Kubernetes, and container orchestration.
• Expertise with Databricks, including ML workflows and data pipeline management.
• Familiarity with tools like MLflow, DVC, Prometheus, and Grafana for versioning and monitoring.
• Experience implementing security and compliance standards for AI systems.
• Strong problem-solving and communication skills, with a collaborative mindset.
• Experience with support and guidance of junior team members
• Fluency in written and spoken English

Preferred Qualifications:

• Experience with large-scale distributed training and fine-tuning of GenAI models.
• Familiarity with prompt engineering and model optimization techniques.
• Contributions to open-source projects in the MLOps or GenAI space.
• Familiarity with PySpark for distributed data processing.