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Tier-1 brand, metro location, and mid-level AI specialization create moderate applicant competition.
Highly specialized agentic ML, RAG, and prompt engineering skills limit cross-industry transferability.
Many mandatory, specific ML/agent skills and model-focused requirements increase filter strictness.
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Design, develop, and maintain multi-agent systems and AI agent capabilities for Asset Management cloud-hosted solutions.
Build and optimize Retrieval-Augmented Generation (RAG) solutions using vector databases and develop frameworks for agent governance, observability, and performance measurement.
Drive technical discussions, collaborate with engineering teams, and implement AI integrations with developer tools and enterprise systems.
Strong hands-on experience with multi-agent orchestration, agent memory management, tool-calling architectures, planning and reasoning systems, and prompt engineering.
Experience working with RAG, embeddings, vectorization, retrieval systems, and developer assist tools such as GitHub Copilot and OpenAI Codex.
Familiarity with foundation AI models including OpenAI GPT, Google Gemini, and Anthropic Claude.
Work Experience Required: Not explicitly mentioned in the JD.
Expertise in building enterprise AI assistants, coding agents, AI copilots, or software engineering productivity platforms at scale.
Strong understanding of AI governance, security, responsible AI, and compliance requirements.
Experience with cloud-native AWS architectures and related technologies like EKS, Docker, API design, microservices preferred but not mandatory.