Senior Expert AI & Data Engineering

Responsibilities

• Responsible to design, build, and operate scalable data and AI pipelines (batch, streaming, and ML) across the full lifecycle, ensuring robustness, reliability, and maintainability from development through production operations.
• Develop and own end-to-end Data & AI solutions, covering data ingestion, transformation, feature engineering, model development, deployment, serving, and lifecycle management, in alignment with Borealis/Borouge International business and technology standards.
• Define, implement, and evolve enterprise data platform architectures (Data Lake, Lakehouse, Warehouse), ensuring scalability, interoperability, performance, and long-term sustainability across analytical and AI use cases.
• Design, develop, deploy, and optimize machine learning and AI models, including predictive, prescriptive, and generative AI solutions, ensuring production-grade quality, explainability, and measurable business impact.
• Establish, operate, and continuously improve MLOps and DataOps practices, including CI/CD pipelines, automated testing, model monitoring, retraining strategies, data quality controls, and versioning across environments.
• Integrate, process, and manage large-scale structured and unstructured data sources, such as IoT data, logs, documents, text, images, and other non-relational data, enabling advanced analytics and AI capabilities.
• Design, build, and expose reusable Data & AI services, including APIs, data products, feature stores, and model endpoints, enabling scalable consumption by analytics, automation, and downstream digital solutions.
• Ensure data quality, governance, security, and compliance across all Data & AI solutions, collaborating with architecture, security, and governance stakeholders to meet Borealis/Borouge International policies and regulatory requirements.
• Monitor, analyze, and optimize performance, reliability, scalability, and cost efficiency of data platforms and AI systems, proactively identifying improvement opportunities and technical risks.
• Act as a senior technical expert and sparring partner for stakeholders, collaborating with business, IT, and digital teams to translate requirements into scalable, production-ready AI and data solutions that drive sustainable, AI-enabled business value.

Tasks

• Education: Masters Degree Computer Science, Engineering or Business
• Specific Microsoft Azure certifications: Azure Fundamentals, Azure Data Engineer Associate, Azure Developer Associate
• Relevant professional experience: > 9 years
• Very strong expertise in cloud-based Data & AI platforms, preferably Microsoft Azure (e.g., Data Factory, Databricks, Synapse, Azure ML or equivalent), with the ability to design and operate enterprise-grade data and AI solutions.
• Advanced experience in designing, building, and operating scalable data pipelines (ETL/ELT), workflow orchestration, and distributed data processing frameworks (e.g., Spark), ensuring performance, reliability, and maintainability.
• Excellent understanding of modern data architectures (Data Lake, Lakehouse, Data Warehouse) and large-scale data processing patterns, including data modeling, storage optimization, and data lifecycle management.
• Excellent programming expertise in Python (mandatory), with additional experience in languages such as Scala or Java, enabling the development of robust, production-grade data and AI solutions.
• Excellent knowledge of SQL and NoSQL technologies, including relational, distributed, and big data systems, with the ability to optimize queries and data access patterns at scale.
• Hands-on expertise in machine learning and AI engineering, including model development, training, evaluation, deployment, and optimization, with a focus on production readiness and business applicability.
• Excellent understanding and practical application of MLOps and DataOps practices, including CI/CD pipelines, automated testing, monitoring, versioning, and data quality management across the full lifecycle.
• Proven experience in working with structured and unstructured data, such as text, images, IoT, and time-series data, enabling advanced analytics and AI use cases.
• Ability to design and develop APIs, microservices, and data/AI products, enabling scalable, secure, and reusable consumption of data and AI capabilities across systems.
• Exposure to Generative AI, LLMs, and advanced AI services is considered a strong advantage, including understanding of modern AI architectures and emerging enterprise use cases.
• Fluent English skills (spoken and written).