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Tier-1 brand, mid-level AI role, and metro location increase candidate competition.
Requires specialized generative AI, knowledge-graph, and regulated-pharma experience, limiting cross-industry transferability.
Explicit 4–7 years plus mandatory Generative AI, cloud, Databricks, and knowledge-graph skills.
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Design, develop, and deploy AI solutions using Generative AI, agentic AI, semantic data, and enterprise platforms focusing on pharmaceutical and operational domains.
Build scalable, enterprise-ready AI products from proof-of-concept prototypes that integrate structured and unstructured data with knowledge graphs and semantic models.
Collaborate with stakeholders to translate business needs into compliant, responsible AI implementations aligned with regulatory requirements and continuous improvement.
Bachelor's or Master's degree in Data Science, AI, Computer Science, Information Science, or related fields.
4 to 7 years of experience in Data Science/AI with hands-on expertise in Generative AI, large language models, and agentic AI.
Proficiency in Python and SQL; experience with AWS cloud services including AWS Bedrock, OpenAI APIs, and Databricks.
Experience with knowledge graph platforms (e.g., Stardog, GraphDB) and applying FAIR data principles in enterprise or pharmaceutical data environments.
Technically strong individual contributor able to move AI solutions from concept to scalable, compliant implementations within regulated pharmaceutical or operational contexts.
Experienced in integrating advanced AI technologies with semantic data modeling and enterprise data ecosystems to solve complex business problems.
Capable of operating cross-functionally with product teams, data engineers, and domain experts to align AI innovation with organizational digital transformation goals.