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Metro data+GenAI role with broad mandatory skills but moderate brand, yielding medium competition.
Requires deep data engineering and GenAI expertise, limiting cross-industry transferability.
Many mandatory, specialized technologies and domain skills required, causing high shortlisting strictness.
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Design, build, and maintain end-to-end batch and real-time data pipelines and cloud data warehouse/lakehouse architectures using Python, PySpark, Databricks, dbt, Airflow, Snowflake, BigQuery, and Delta Lake.
Develop and deploy production-grade AI and GenAI systems including RAG pipelines, LLM-based automation workflows, and distributed agent-based systems with compliance and explainability features.
Manage infrastructure and integration of data and AI pipelines on Kubernetes with CI/CD, API microservices, monitoring, caching (Redis), and query optimization for enterprise-scale reliability.
Strong proficiency in Python, PySpark, SQL, dbt, and Apache Airflow for data engineering workflows.
Experience with cloud data warehouses (Snowflake, BigQuery, or Redshift) and associated optimization techniques.
Hands-on experience building and deploying production-grade AI/GenAI systems including RAG pipelines and LLM orchestration frameworks.
Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or related technical disciplines; Work Experience Required: Demonstrable experience in data or AI engineering roles with increasing ownership (exact years not specified).
Experienced in full data-to-intelligence stack operations combining rigorous data engineering and advanced AI/GenAI engineering.
Capable of managing scalable, production-grade AI systems with complex reasoning, explainability, and compliance requirements in enterprise environments.
Comfortable working with cloud infrastructure, Kubernetes deployments, and building data/AI APIs with a focus on reliability and observability.