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Strong employer brand, general ML title, and Hyderabad metro increase candidate density.
Biotech/manufacturing data and LLM-focused engineering needs make skills less transferable across industries.
Explicit 8-10 years plus mandatory Databricks, PySpark, LLM, and domain-specific experience increases filter strictness.
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Design, develop, and optimize scalable, secure data pipelines and ingestion solutions supporting manufacturing-related AI and analytics applications.
Build and maintain ETL/ELT pipelines using Databricks, PySpark, Scala, and SQL to process large-scale and diverse data sources including structured and unstructured data.
Implement data governance, security, privacy, and interoperability frameworks across hybrid cloud environments, enabling self-service analytics and AI-driven insights.
Master's degree with 6-8 years experience OR Bachelor's degree with 8-10 years experience in Computer Science, IT, or related field.
Strong hands-on experience with Databricks, Apache Spark (PySpark, SparkSQL), AWS, Python, SQL, and scalable big data processing technologies.
Experience with Scaled Agile Framework (SAFe) and Agile/DevOps practices in software/data engineering lifecycle.
Experience with streaming technologies such as Apache Kafka or Debezium for real-time data integration.
Proven ability to work with complex, large-scale data architectures supporting manufacturing or biotech/pharma domains.
Experience collaborating with ML engineers, product managers to build AI and LLM feature engineering workflows including vector databases and knowledge graphs.
Skilled in operationalizing data pipelines with governance, security, logging, and optimizing latency in hybrid cloud environments for enterprise applications.