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Tier-1 employer, popular ML title, mid-level experience, metro location increase candidate competition.
Core ML and data engineering skills are widely transferable, though pharma domain knowledge is beneficial.
Explicit 5–7 years plus multiple mandatory technologies (Python, Spark, AWS, dbt, Databricks) increases filtering strictness.
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Build and maintain data pipelines using Spark, Python, SQL, and AWS services to enable rapid prototyping of AI/ML applications from operational source systems.
Develop, train, and integrate machine learning models and GenAI capabilities, including LLM-based agents, to support predictive, classification, anomaly detection, and optimization use cases.
Collaborate closely with software engineers, systems analysts, and business stakeholders to translate requirements into practical, prototype-ready AI and data solutions following Medallion architecture patterns.
5–7 years of hands-on experience in data engineering and/or applied AI/ML roles.
Proficiency in Python, Spark, SQL, and AWS cloud services (such as S3, Glue, Lambda) for building data pipelines and cloud-native workflows.
Experience with dbt for data transformation, testing, and documentation, and Databricks platform for model development and deployment.
Bachelor's or Master's degree in Computer Science, Data Science, Computer Engineering, Information Systems, or a related field.
Experienced in handling real-world operational data from multiple source systems in fast-paced, prototyping environments prioritizing speed and usefulness over enterprise-level perfection.
Strong knowledge of Medallion data architecture and practical application of AI/ML models integrated into user-facing applications within innovation labs or digital teams.
Skilled at collaborating across technical and non-technical stakeholders to deliver practical AI-powered prototypes leveraging GenAI and LLM techniques aligned with business needs.