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Niche GenAI/Agent skills reduce candidate pool despite metro location.
GenAI, LLM, and vector-database skills are specialized but broadly transferable across industries.
Multiple specific GenAI, LLM, deployment and vector DB skills required but no explicit years specified.
Develop, test, and manage end-to-end execution of Agentic AI and GenAI projects ensuring quality and adherence to architectural designs.
Translate AI architectural blueprints into prototypes and production-ready services, including GenAI apps, RAG pipelines, and agent-based solutions using frameworks like Langchain, LangGraph, and Google ADK.
Optimize and deploy scalable Agentic AI models using cloud platforms (AWS, Azure, GCP), debug performance issues, and enforce best engineering practices.
Proficient in AI/ML engineering or software development with Python and related frameworks.
Experience delivering projects with GenAI, Large Language Models (LLMs), Transformer models, and Agentic AI solutions.
Familiarity with prompt engineering, APIs, data preprocessing, model integration, vector databases, RAG architectures, and MCP/Agent to Agent Communication.
Knowledge of SQL, NoSQL, data warehousing, CI/CD pipelines, Docker, and Kubernetes for AI deployment. Work Experience Required: Not explicitly mentioned in the JD.
Demonstrates hands-on expertise designing and optimizing Agentic AI workflows and scalable GenAI production systems.
Experienced in integrating advanced AI models with cloud infrastructure and containerized environments for deployment.
Able to manage complex AI projects from prototype to production maintaining quality, scalability, and performance.