control, code review), and with project management tools (JIRA or similar) in an agile environment. Familiarity with other ML Ops tools (Kubeflow, MLflow, etc.) or big data processing frameworks (Spark) can be an added advantage Rewards and Benefits We believe in supporting our employees in both their professional and personal lives. As part of our commitment to your well More ❯
Experience 10+ years' experience in Data Engineering, with a minimum of 3 years of hands-on Azure Databricks experience delivering production-grade solutions. Strong programming proficiency in Python and Spark (PySpark) or Scala, with the ability to build scalable and efficient data processing applications. Advanced understanding of data warehousing concepts, including dimensional modelling, ETL/ELT patterns, and modern More ❯
familiarity with data science libraries and frameworks. Strong expertise in statistical modelling, predictive analytics, and machine learning. In-depth understanding of data architecture and big data technologies (e.g., Hadoop, Spark, AWS). Exceptional problem-solving skills and ability to think strategically. Outstanding communication abilities to convey technical concepts to non-technical stakeholders. Experience in managing cross-departmental or large More ❯
record of deploying AI into production at scale. Technical Skills: Strong backend Python; expertise with ML frameworks (TensorFlow, PyTorch, Scikit-Learn, etc.); familiarity with modern data pipeline tools (Airflow, Spark, Kafka) and workflow orchestration frameworks such as n8n or LangGraph. Applied AI: Background in enterprise applications such as chatbots, workflow automation, RAG pipelines, or intelligent document processing. Domain Awareness More ❯
