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Niche GenAI skills but metro location and senior mid-level range produce moderate competition.
Specialized GenAI/LLM skills increase domain bias but remain moderately transferable across industries.
Explicit 6-14 years plus mandatory LLM, vector DB, Python and API experience enforces strict filters.
Develop and maintain Generative AI and Large Language Model (LLM)-based systems, including prompt engineering and agentic AI workflows.
Design and implement Retrieval-Augmented Generation (RAG) architectures with vector databases such as Pinecone, FAISS, Weaviate, or OpenSearch.
Integrate and optimize AI solutions using APIs from OpenAI, Azure OpenAI, Anthropic, or Google GenAI, ensuring performance, cost management, and validation of AI outputs.
6 to 14 years of overall software development experience with strong proficiency in Python.
2 to 3 years of hands-on experience with Generative AI/LLM-based systems and prompt engineering.
Experience with RAG architectures and vector databases (e.g., Pinecone, FAISS, Weaviate, OpenSearch).
Hands-on experience with AI APIs (OpenAI, Azure OpenAI, Anthropic, or Google GenAI), plus working knowledge of APIs, microservices, SQL/NoSQL databases, and cloud platforms (AWS, Azure, or GCP).
Experienced in building autonomous or multi-agent AI solutions leveraging agentic AI workflows.
Strong familiarity with LLM fundamentals including tokenization, context windows, cost/performance trade-offs, hallucinations, and output validation.
Comfortable navigating cloud environments and integrating diverse AI-related technologies including vector databases and microservices architecture.