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Tier-1 brand plus metro location and popular LLM skillset increases applicant density.
Core ML/AI skills are transferable, but MCP/Bedrock and regulated pharma context increase domain specificity.
Explicit 1–3 years plus mandatory AWS Bedrock, AgentCore, and MCP experience tightens filters.
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Design, develop, and deploy autonomous multi-agent AI systems to solve complex pharmaceutical R&D workflows using platforms like AWS Bedrock and AWS AgentCore.
Build and maintain scalable agentic AI architectures with tool integrations (APIs, databases) and Model Context Protocol (MCP) for standardized data interactions.
Collaborate with cross-functional teams, write production-grade Python code, implement CI/CD and observability frameworks, and mentor junior engineers in AI engineering best practices.
Bachelor's degree in Computer Science, Software Engineering, AI, Data Science, or related technical field.
1–3 years of hands-on experience building AI/ML or LLM-based applications, including at least 1 year with agentic AI systems or multi-agent frameworks.
Strong proficiency in Python 3.9+ and experience with LLM frameworks (LangChain, LlamaIndex, AutoGen, CrewAI) and managed AI platforms (AWS Bedrock, AWS AgentCore).
Experience with REST APIs, vector databases (Pinecone, Weaviate, Chroma, etc.), and cloud services (AWS).
Engineer with demonstrated ability to deliver production-grade autonomous AI agent solutions end-to-end in regulated or scientific environments.
Experience integrating AI systems into complex enterprise digital ecosystems with cross-disciplinary collaboration (science, product, regulatory).
Strong technical skills in agent architecture design, MCP-based integration, and scalable AI operations on cloud-native platforms, emphasizing operational reliability and AI safety.