Responsibilities

• Develop and lead a company-wide AI and automation strategy aligned with business goals.
• Build and manage a high-performing team of data scientists, ML engineers, and automation specialists.
• Oversee research and development of advanced AI models (predictive analytics, NLP, computer vision, etc.) to drive innovation and efficiency.
• Define and prioritize AI initiatives — setting clear goals, timelines, and success metrics to ensure measurable business value.
• Collaborate closely with data engineering and IT teams to ensure AI solutions have access to high-quality, well-structured data.
• Integrate AI and automation pipelines into existing workflows and infrastructure.
• Act as a strategic liaison between AI teams and business units, translating business needs into technical solutions.
• Evaluate and implement automation tools (e.g., n8n, Make, Zapier, Airflow) to streamline internal processes.

• Establish partnerships with AI vendors, research institutions, and represent the company at industry events.

Requirements

• 5+ years of experience in AI, machine learning, or data science, with at least 2+ years in a leadership or head-level role.
• Proven track record of defining and executing AI strategies that deliver measurable impact.
• Strong knowledge of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn, etc.).
• Experience with automation and orchestration tools such as n8n, Make (Integromat), Zapier, Airflow, or similar.
• Hands-on experience building data pipelines and integrating AI models into production environments.
• Excellent understanding of data architecture, MLOps, and cloud environments (AWS, GCP, or Azure).
• Strong leadership, communication, and stakeholder management skills.
• Strategic mindset with the ability to balance technical depth and business objectives.

• Fluent English (B2+ or higher).

Nice to Have

• Experience with RPA platforms (UiPath, Automation Anywhere, etc.).
• Familiarity with LLM development and GenAI integration (OpenAI, Anthropic, etc.).
• Background in data engineering or software architecture.
• Contributions to AI communities, research, or open-source projects.