Back
Niche GenAI skills reduce competition, but metro location and known employer increase applicant density.
Requires specialized GenAI, LLM, and vector DB expertise, limiting cross-industry transferability.
Multiple mandatory GenAI, vector DB, cloud, and CI/CD technical requirements enforce strict screening.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and build end-to-end Generative AI (GenAI) solutions including RAG pipelines, agentic workflows, and multimodal use cases for enterprise applications.
Translate business requirements into GenAI solution blueprints and implementation roadmaps, ensuring scalable, secure, and production-ready deployments.
Provide technical leadership, mentor engineering teams, and collaborate with stakeholders to align solutions with business outcomes and production timelines.
Strong hands-on and architectural expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, and agentic AI frameworks such as LangChain and LangGraph.
Experience with cloud-native GenAI architecture and deployments including containerization (Docker), orchestration (Kubernetes), CI/CD pipelines, and cloud security best practices.
Deep understanding of vector similarity search tools like Pinecone, FAISS, Weaviate, Chroma, Milvus, or Azure AI Search Elastic vectors.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in architecting enterprise GenAI use cases such as document Q&A, summarization, customer support assistants, and workflow automation with metrics-based evaluation.
Comfortable operating cross-functionally with business, data science/ML teams, and engineering for solution delivery in fast-evolving AI environments.
Skilled in deploying responsible AI practices covering security, governance, privacy, compliance, and monitoring within cloud-based GenAI platforms.