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).