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Specialized GenAI skillset reduces applicant density, but seniority and metro location create medium competition.
GenAI/LLM skills are transferable but require specialized tooling and domain knowledge, so background fit is medium.
Explicit years plus mandatory Python, LLM, RAG, and vector DB requirements imply high shortlisting strictness.
Develop and enhance Generative AI/LLM-based systems leveraging prompt engineering, RAG architectures, and vector databases.
Build and deploy autonomous/multi-agent AI workflows and solutions with a focus on operational efficiency and accuracy.
Manage end-to-end integration of GenAI functionalities using APIs, microservices, and cloud platforms for scalable applications.
6 - 14 years of overall software development experience with strong proficiency in Python.
2 - 3 years hands-on experience with Generative AI/LLM systems including prompt engineering.
Practical experience with RAG architectures, vector databases (Pinecone, FAISS, Weaviate, or OpenSearch), and Agentic AI workflows.
Hands-on knowledge of LLM fundamentals (tokenization, context windows, hallucinations) and experience with OpenAI, Azure OpenAI, Anthropic, or Google GenAI APIs.
Experienced in building and scaling autonomous AI/LLM-driven solutions in complex environments with practical deployment exposure.
Comfortable working across cloud platforms (AWS, Azure, GCP) and integrating AI services through APIs and microservices.
Possesses deep understanding of LLM operational challenges and trade-offs, with ability to optimize for performance, cost, and accuracy.