AI/ML Solutions Engineer

Навыки

Обязанности

• 01Implement AI/ML solutions to automate business processes, focusing on measurable business impact (reduction of manual effort, optimization of operational costs)
• 02Ensure high reliability, accuracy, and performance of automated systems in line with SLA requirements and business objectives
• 03Drive rapid delivery of new automation use cases from idea definition and validation to production deployment
• 04Develop scalable and reusable AI automation components and solutions
• 05Evaluate and demonstrate the business impact of implemented solutions using quantitative metrics (process improvements, resource savings, user adoption and engagement)
• 06Support business teams with expertise related to business processes, operational efficiency, and customer support effectiveness
• 07Identify automation opportunities and design comprehensive AI/ML solutions to address business needs
• 08Collaborate with external vendors on integrations, customizations, and solution delivery when required
• 09Monitor operational workflows, identify bottlenecks, and define areas for improvement
• 10Document solutions, processes, and outcomes, ensuring transparency through regular reporting and process updates
• 11Monitor and evaluate system performance, continuously improving automation workflows, logic, and user scenarios
• 12Collaborate with business and technical stakeholders to integrate AI solutions into existing operational processes

Требования

• 013+ years of experience in AI/ML engineering, automation, applied data science, or business analytics
• 02English 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
• 03Proven experience implementing AI solutions for real-world business automation use cases
• 04Hands-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
• 05Strong knowledge of Natural Language Processing (NLP) and experience working with unstructured data (e.g., text, logs, documents)
• 06Proficiency with machine learning and data analysis tools: Python, scikit-learn, TensorFlow/PyTorch, SQL, Pandas
• 07Practical knowledge of MLOps tools and practices (e.g., Docker, MLflow, CI/CD pipelines) and DataOps principles
• 08Understanding of REST APIs, enterprise integration patterns, and database structures
• 09Experience with automation platforms (e.g., UiPath, Power Automate) or developing custom automation scripts
• 10Knowledge of best practices for structuring and preparing data for AI applications
• 11Experience with data analysis and visualization tools (e.g., Excel, SQL) to evaluate model performance and generate actionable insights
• 12Familiarity with IT infrastructure, customer support software platforms, and ITIL principles
• 13Ability to create technical and process documentation: specifications, architecture diagrams, user guides, and operational documentation
• 14Strong analytical skills and the ability to translate business challenges into practical AI-driven solutions
• 15Excellent communication skills for effective collaboration with internal teams, external vendors, and business stakeholders
• 16Knowledge of customer support best practices and operational efficiency management
• 17Confident user of MS Office, PowerPoint, Jira, and Confluence