Senior AI Engineer

SmartRecruiters Inc. delivers an AI-powered hiring platform built for global scale, automating and optimizing the entire talent acquisition process. The company serves more than 4,000 clients including LinkedIn, McDonald's, VISA, CD Projekt Red, and Allegro. In 2025, SmartRecruiters joined SAP to accelerate AI innovation in hiring within SAP's ecosystem. The R&D structure is based on empowered product teams responsible for business outcomes and customer satisfaction.

• BSc or MSc in Computer Science, Machine Learning, AI, NLP, or related technical field, or equivalent practical experience
• 5+ years of experience developing and deploying machine learning systems in production
• Proven expertise in NLP techniques, LLM fine-tuning, and model evaluation
• Strong proficiency with deep learning frameworks (PyTorch, TensorFlow) and ML tools (scikit-learn, Hugging Face)
• Advanced Python programming skills and solid software engineering fundamentals
• Experience designing and maintaining end-to-end ML pipelines from data ingestion to deployment
• Hands-on experience with agentic workflows or building autonomous AI systems is a major plus
• Familiarity with chatbots, conversational AI, or multi-agent systems is strongly preferred
• Experience with data quality management, evaluation metrics, or observability in ML systems is a plus
• Strong analytical thinking, creativity, and ownership mindset
• Excellent communication and collaboration skills in a cross-functional, international environment
• Fluency in English, both written and spoken

• Architect and build agentic workflows combining large language models, reasoning components, and data pipelines to create adaptive, goal-driven conversational systems
• Lead design and development of advanced ML/NLP products from ideation to production, including model training, evaluation, optimization, and deployment
• Drive experimentation with new approaches for agentic reasoning, coordination, and autonomous system design
• Collaborate with product and engineering teams to integrate intelligent agents into recruiting workflows
• Guide technical decisions, mentor engineers, and define best practices for scalable, maintainable ML systems
• Contribute to the evolution of NLP and conversational intelligence stack focusing on reliability, personalization, and user impact
• Identify opportunities to automate, improve, and innovate within the chat-based ecosystem