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ai-инструменты
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описание
This role is focused on building intelligent, scalable data platforms that power enterprise analytics, automation, and business insights. You will design and develop AI-powered data pipelines, integrate Large Language Models (LLMs) using orchestration frameworks, and create metadata-driven architectures for structured and unstructured data sources. The position requires hands-on engineering expertise to deliver robust, reusable solutions that support analytics, data discovery, and transformation across distributed, cloud-native environments.
задачи
Design, develop, and maintain AI-powered data pipelines for automation, metadata enrichment, schema understanding, and intelligent data transformation.
Build and integrate LLM-based applications and AI services using orchestration or agent frameworks.
Develop scalable Python microservices, APIs, and data-processing workflows.
Create reusable semantic models, metadata frameworks, and architectures to support analytics and reporting.
Design and optimize ETL/ELT pipelines, data integration workflows, and star-schema generation for diverse data sources.
Implement intelligent data discovery, mapping, and transformation solutions across enterprise systems.
Collaborate with data architects, analysts, and governance teams to align technical solutions with business objectives.
Establish engineering best practices for scalability, observability, performance, security, testing, and deployment.
Support cloud-native and distributed data platforms using modern data engineering and AI technologies.
Mentor engineers, participate in code reviews, and contribute to technical leadership and architectural decisions.
требования
6+ years of experience in software engineering, AI engineering, or data engineering roles.
Strong proficiency in Python and backend development.
Hands-on experience building AI/LLM-powered applications or intelligent automation solutions.
Experience with modern AI frameworks, orchestration tools, or agent-based architectures.
Solid understanding of data engineering concepts: ETL/ELT pipelines, data modeling, metadata management, analytics platforms.
Experience with cloud platforms (Azure, AWS, or GCP).
Familiarity with scalable data platforms, APIs, distributed systems, and microservices.
Strong analytical, problem-solving, and communication skills.
Будет плюсом: Experience with knowledge graphs, semantic technologies, metadata platforms, or enterprise data modeling; Familiarity with MLOps, LLMOps, model evaluation, AI observability, or responsible AI practices; Experience with Spark, Lakehouse architectures, or modern analytics ecosystems; Exposure to containerization, CI/CD pipelines, and cloud-native deployment patterns; Experience building AI-enabled analytics, search, recommendation, or natural language query solutions
