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Strong global brand, mid-level 5+ years, and generalist ML/data technical lead role make competition high.
Technical ML and data engineering skills are transferable across industries but require domain knowledge, so medium.
Multiple mandatory tech stacks, cloud and ML framework requirements and explicit 5+ years make shortlisting high.
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Build and maintain data pipelines and data warehousing solutions that support AI/ML initiatives.
Develop, implement, deploy, and monitor machine learning models to solve business problems and optimize performance.
Collaborate with business users to understand problems and communicate AI solutions and findings to stakeholders.
Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related field.
5+ years of experience in data engineering or data science.
Proficiency in programming languages Python, SQL, and Java/Scala; experience with data engineering tools (Spark, Hadoop, Kafka, Airflow); machine learning frameworks (TensorFlow, PyTorch, scikit-learn); and cloud platforms (AWS, Azure, GCP).
Experience with RDMS and Vector DB; strong analytical, problem-solving, and communication skills.
Experienced professional combining data engineering and data science expertise with a focus on scalable cloud-native solutions for telecommunications or large-scale platforms.
Skilled at end-to-end AI/ML lifecycle management from data ingestion to model deployment and monitoring, emphasizing performance and scalability.
Capable of leading discussions with business stakeholders to design AI solutions addressing complex business challenges in a global, high-impact technology environment.