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Strong employer brand plus niche LLM/agent requirements yield moderate competition.
Core LLM/agent skills transfer, but pharmaceutical compliance and domain knowledge moderately constrain fit.
Explicit 1–3 years, specific agentic AI platforms, and regulated-industry expectations enforce strict filters.
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Design, develop, and deploy autonomous multi-agent AI systems for pharmaceutical R&D workflows using AWS Bedrock, AgentCore, and MCP frameworks.
Build and integrate LLM-based applications and Retrieval-Augmented Generation pipelines grounded in proprietary scientific data.
Own end-to-end delivery including software engineering best practices, API development, CI/CD, monitoring, and collaboration with cross-disciplinary teams.
Bachelor's degree in Computer Science, AI, Data Science, or related technical field (Master's preferred).
1–3 years of experience building AI/ML or LLM-based applications; at least 1 year with agentic AI systems or multi-agent frameworks.
Proficient in Python 3.9+, experience with AI frameworks (LangChain, LlamaIndex, etc.) and managed AI platforms (AWS Bedrock, AgentCore).
Work Experience Required: 1–3 years in relevant AI software engineering role.
Experienced in architecting and deploying autonomous agentic AI systems leveraging multi-agent collaboration and tool integration in regulated, scientific domains.
Proficient with AWS native AI platforms (Bedrock, AgentCore) and Model Context Protocol (MCP) to standardize AI workflows with internal and external data sources.
Capable of translating complex pharmaceutical R&D challenges into scalable AI solutions, working closely with scientific and regulatory stakeholders.