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Strong employer brand plus metro location increases competition, despite specialized GenAI skills.
Role requires niche generative AI and agentic expertise, limiting easy cross-industry transferability.
Explicit 5–7 years plus many mandatory, specialized GenAI, agentic, and deployment skills makes filters strict.
Design and implement advanced Generative AI and agentic AI solutions integrated into enterprise Controls Technology platform.
Architect and optimize systems including context engineering, Retrieval-Augmented Generation (RAG), knowledge graphs, multi-agent orchestration, and agent harnesses to enhance automation and operational efficiency.
Ensure deployment scalability, observability, compliance with ethical AI standards, and mentor junior developers within AI-driven project environments.
5–7 years of experience in AI or software development with significant Generative AI and agentic AI expertise.
Bachelor's or master's degree in Computer Science, Data Science, AI, or related field.
Proficiency in Python, containerization (Docker), orchestration (Kubernetes), CI/CD pipelines, and cloud infrastructure (e.g., AWS).
Solid hands-on experience with Generative AI concepts, prompt and context engineering, RAG systems, knowledge graphs, agentic AI systems (Google ADK or equivalents), multi-agent orchestration, agent interoperability protocols (MCP, A2A), and GenAI APIs/frameworks (OpenAI, LangChain, LlamaIndex).
Experienced technologist specializing in Generative AI and agentic AI systems with a strong background in architecting scalable production AI solutions.
Demonstrates expertise in advanced multi-agent orchestration, knowledge graph application, and complex context engineering for reliable and efficient AI deployments.
Capable of leading technical initiatives, ensuring AI compliance and ethical standards, and mentoring team members in a cross-functional environment focused on business-driven AI innovation.