Requirements

Expected technologies

SQL

Python

AI/ML expertise

Cloud platform

Our requirements

• 7+ years of relevant professional experience in data science, machine learning, or AI engineering.
• Proven track record of building and deploying sophisticated machine learning solutions, with expertise in agentic systems, NLP, or deep learning.
• Demonstrated experience in architecting scalable data pipelines and ML platforms to support high-volume model training and inference.
• Strong proficiency in Python (scikit-learn, TensorFlow, PyTorch, Hugging Face) for both research and production environments.
• Hands-on experience with Retrieval-Augmented Generation (RAG), model fine-tuning, Reinforcement Learning from Human Feedback (RLHF), and implementation of model evaluation frameworks and drift detection.
• Advanced SQL skills, with expertise in data warehousing, ETL processes, and data governance best practices.
• Solid understanding of database architectures and trade-offs, combined with cloud platform experience (AWS, GCP, Azure) and distributed computing frameworks (Spark, Dask).
• Ability to identify high-impact AI opportunities and translate them into technical solutions that deliver measurable business value.
• Strong curiosity and continuous learning mindset, with interest in cutting-edge AI research and i

Your responsibilities

• Design and implement a foundational AI data platform with a focus on scalability, reliability, and high performance.
• Build and deploy production-ready, autonomous systems that execute marketing strategies.
• Create robust MLOps pipelines for scalable model training, deployment, and continuous improvement.
• Prototype and deploy state-of-the-art machine learning models, with a specialization in agentic AI systems for marketing automation.
• Enhance large language model (LLM) capabilities through techniques such as RAG, fine-tuning, RLHF, and advanced evaluation methods.
• Research and integrate emerging AI technologies, translating academic innovations into practical business applications.
• Perform deep statistical analysis to uncover patterns and metrics that drive successful marketing campaigns.
• Deliver end-to-end data science products, from proof-of-concept through to production deployment.
• Collaborate closely with product and engineering teams to integrate AI models into scalable, production-ready systems.