Мы в Telegram
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.