AI/ML Solutions Engineer

Our team (Customer Support Efficiency) is responsible for improving the performance and scalability of the Customer Support organization. We optimize business processes, own and evolve support tools and platforms, analyze support quality and operational metrics, and drive automation initiatives—from workflow improvements to AI-powered solutions. Our team works at the intersection of business, technology, and data to help Support deliver faster, higher-quality, and more efficient customer experiences.

Responsibilities:

• Implement AI/ML solutions to automate business processes, focusing on measurable business impact (reduction of manual effort, optimization of operational costs).
• Ensure high reliability, accuracy, and performance of automated systems in line with SLA requirements and business objectives.
• Drive rapid delivery of new automation use cases from idea definition and validation to production deployment.
• Develop scalable and reusable AI automation components and solutions.
• Evaluate and demonstrate the business impact of implemented solutions using quantitative metrics (process improvements, resource savings, user adoption and engagement).
• Support business teams with expertise related to business processes, operational efficiency, and customer support effectiveness.
• Identify automation opportunities and design comprehensive AI/ML solutions to address business needs.
• Collaborate with external vendors on integrations, customizations, and solution delivery when required.
• Monitor operational workflows, identify bottlenecks, and define areas for improvement.
• Document solutions, processes, and outcomes, ensuring transparency through regular reporting and process updates.
• Monitor and evaluate system performance, continuously improving automation workflows, logic, and user scenarios.
• Collaborate with business and technical stakeholders to integrate AI solutions into existing operational processes.

Required Experience and Skills:

• 3+ years of experience in AI/ML engineering, automation, applied data science, or business analytics.
• English proficiency at least at B2 (Upper-Intermediate) level, with strong written and verbal communication skills, including email communication and participation in video conferences with English-speaking stakeholders.
• Proven experience implementing AI solutions for real-world business automation use cases.
• Hands-on experience with Large Language Model (LLM)-based systems, including AI agents, RAG architectures, prompt engineering, fine-tuning, evaluation methodologies, and iterative improvement based on user feedback and business KPIs.
• Strong knowledge of Natural Language Processing (NLP) and experience working with unstructured data (e.g., text, logs, documents).
• Proficiency with machine learning and data analysis tools: Python, scikit-learn, TensorFlow/PyTorch, SQL, Pandas.
• Practical knowledge of MLOps tools and practices (e.g., Docker, MLflow, CI/CD pipelines) and DataOps principles.
• Understanding of REST APIs, enterprise integration patterns, and database structures.
• Experience with automation platforms (e.g., UiPath, Power Automate) or developing custom automation scripts.
• Knowledge of best practices for structuring and preparing data for AI applications.
• Experience with data analysis and visualization tools (e.g., Excel, SQL) to evaluate model performance and generate actionable insights.
• Familiarity with IT infrastructure, customer support software platforms, and ITIL principles.
• Ability to create technical and process documentation: specifications, architecture diagrams, user guides, and operational documentation.
• Strong analytical skills and the ability to translate business challenges into practical AI-driven solutions.
• Excellent communication skills for effective collaboration with internal teams, external vendors, and business stakeholders.
• Knowledge of customer support best practices and operational efficiency management.
• Confident user of MS Office, PowerPoint, Jira, and Confluence.