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Strong employer brand, generalist ML title, metro location, and broad skill requirements increase competition.
Mix of LLM/ML and manufacturing data engineering creates moderate domain specificity, limiting cross-industry transferability.
Explicit years plus mandatory Databricks/PySpark/LLM and data engineering skills make shortlisting highly selective.
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Design, develop, and maintain scalable ETL/ELT data pipelines using Databricks, PySpark, Scala, and SQL for Manufacturing Applications.
Develop and optimize ML/LLM feature engineering workflows including data ingestion, embeddings/vector databases, RAG/LLM serving, and knowledge graph/metadata integration.
Implement secure, governed, and interoperable data access solutions across hybrid cloud environments, supporting analytics and AI-driven insights in biotech/pharma/manufacturing domains.
Master’s degree with 4-6 years or Bachelor’s with 6-8 years in Computer Science, IT or related field.
Hands-on experience with data engineering tools: Databricks, PySpark, SparkSQL, Apache Spark, AWS, Python, SQL.
Experience with Scaled Agile Framework (SAFe), Agile delivery, and DevOps practices including CI/CD and automated testing.
Work Experience Required: 4-8 years as specified based on degree; experience in biotech/pharma/manufacturing data environments preferred but not strictly mandated.
Experienced in building large-scale, real-time big data pipelines and complex ML/LLM-driven data solutions in manufacturing or biotech/pharma sectors.
Proficient in hybrid cloud data architectures with governance, security, and performance tuning expertise.
Capable of operating effectively in Agile/SAFe environments, collaborating cross-functionally with product managers, ML engineers, and operations teams.