Data Engineer / Data Scientist (Cloud, AI & Analytics) - 23219

Data Engineer / Data Scientist (Cloud, AI & Analytics) with strong hands-on experience across cloud data engineering, advanced analytics, and artificial intelligence.
This role requires deep technical expertise in building scalable data pipelines, developing enterprise data solutions, and implementing production-grade AI systems "including LLMs and generative AI "to drive business insight and automation.
Key Responsibilities
Build, maintain, and optimize scalable data pipelines and data models to support analytics, AI, and business intelligence use cases.
Develop and enhance ETL/ELT pipelines using Azure Data Factory (ADF) and cloud-native data integration patterns.
Develop and manage cloud data solutions using Snowflake for high-performance, cost-efficient analytics workloads.
Build and deploy AI and machine learning solutions, including deep learning, NLP, large language models (LLMs), and generative AI applications.
Perform AI experimentation, model development, and prototyping to solve business problems and identify automation opportunities.
Develop and operationalize end-to-end ML pipelines, from data preparation and feature engineering to deployment and monitoring.
Create dashboards and analytical applications that deliver actionable insights and support business decision-making.
Implement and maintain CI/CD pipelines to automate data and AI workflows, ensuring reliability, reproducibility, and faster releases.
Collaborate with engineering, analytics, product, and business teams to integrate data and AI solutions into enterprise applications.
Ensure adherence to data engineering, analytics, and AI best practices, including governance, quality, security, and observability.
Stay current with emerging data, AI, and LLM technologies to drive continuous improvement and innovation.
Required Skills
Strong proficiency in Python for data engineering, analytics, and machine learning development.
Hands-on experience with Snowflake for cloud data warehousing and analytics. Experience developing and implementing data pipelines using Azure Data Factory (ADF) or equivalent ETL/ELT tools.
Solid experience with AI/ML techniques, including deep learning, NLP, and LLMs / Generative AI.
Experience building analytical models, experiments, and AI-driven solutions for real-world business problems.
Strong understanding of data modeling, data quality, and scalable data pipeline development.
Experience implementing CI/CD pipelines for data and ML workflows using modern DevOps practices.
Experience building dashboards and analytical applications using modern BI or visualization tools (Tableau/Power BI).
Familiarity with cloud platforms (Azure preferred; AWS/GCP acceptable).
Strong communication skills to translate complex technical concepts into business insights.
Preferred / Nice-to-Have Skills
Experience with MLOps practices, model deployment, monitoring, and observability. Experience with telecom network data (5G, LTE, VoLTE, SMS, data usage, roaming). Familiarity with vector databases, embeddings, and retrieval-augmented generation (RAG) patterns. Experience integrating LLMs with enterprise data and applications.