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Tier-1 employer, mid-level AI role in metro with broad LLM and data skills increases competition.
Core LLM, Python, and cloud skills are transferable, though pharmaceutical regulatory experience raises domain specificity.
Explicit 4–7 years and many mandatory AI, cloud, Databricks, and knowledge-graph skills increase filter strictness.
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Design, develop, and deploy AI solutions using Generative AI, large language models, and agentic AI to solve high-value business and operational challenges within a highly regulated pharmaceutical context.
Build and scale AI-driven products combining structured and unstructured data, semantic data models, and knowledge graph technologies to enable automation, improved decision-making, and new workflows.
Collaborate with stakeholders to translate ambiguous business problems into scalable, compliant AI solutions leveraging cloud platforms like AWS (including AWS Bedrock), Databricks, and OpenAI APIs.
4 to 7 years of experience in Data Science, AI, Computer Science, or related fields.
Proficiency in Generative AI, large language models, Python, SQL, and AWS cloud services including AWS Bedrock.
Experience with knowledge graphs and semantic data technologies such as Stardog or GraphDB.
Work Experience Required: 4 to 7 years in relevant domains (AI, Data Science, Computer Science, or Information Science).
Technically strong AI practitioner comfortable moving from prototype to production in regulated pharmaceutical or operational data environments.
Experienced in integrating enterprise data ecosystems with semantic modeling and AI to deliver measurable business value and operational improvements.
Able to operate across multidisciplinary teams, translate complex business needs into AI solutions, and implement responsible AI practices aligned with compliance and governance frameworks.