Responsibilities:

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• Perform data analysis, exploration, and preparation to support business use cases.
• Design and develop data pipelines in Python/Databricks and work with cloud data platforms.
• Apply Generative AI techniques to build prototypes, create insights, and enhance solutions.
• Collaborate with cross-functional teams to understand requirements and translate them into data-driven/AI-driven outcomes.
• Conduct unit testing, validation, and data quality checks for datasets, pipelines, and AI outputs.
• Design and develop RESTful APIs to enable communication between different software components.
• Follow agile and DevOps practices for continuous development, testing, and deployment.
• Contribute to data governance, documentation, and best practices for sustainable data and AI solutions.

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Requirements:

• Strong proficiency in Python, with focus on both data engineering (pandas, PySpark, Databricks) and AI (transformers, LLM frameworks).
• Good understanding of SQL and ability to work with relational and non-relational data.
• Familiarity with cloud platforms (AWS, Azure, or GCP) and data lake/warehouse concepts.
• Exposure to Generative AI tools and frameworks (e.g.LangGraph, LangChain, OpenAI APIs).
• Awareness of prompt engineering, fine-tuning, embeddings, and LLM evaluation.
• Familiarity with BI/visualization tools (Power BI, Tableau, or similar).
• Understanding of modern practices like DataOps, MLOps, and AI/GenAI Ops.
• Strong analytical and problem-solving skills with ability to handle ambiguity.
• Excellent written, verbal, and interpersonal communication skills.
• Ability to work effectively with global teams across multiple regions and time zones.
• Curiosity to learn and apply emerging data engineering and Generative AI technologies.

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