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Niche LLM/agent skills and metro hiring produce moderate applicant competition.
Advanced GenAI and LLM specialization yields high domain specificity, limiting cross-domain transferability.
Explicit 7-12 years and mandatory LLM/vector DB toolset raise hiring filter strictness.
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Design and build GenAI/agentic AI systems including chat copilots, workflow/graph agents, and tool integrations.
Implement techniques such as chunking, hybrid search, vector stores, reranking, feedback loops, and continuous evaluation of data quality.
Select, integrate, fine-tune, and monitor LLMs and multimodal models; apply prompt-engineering and manage model guardrails including PII redaction.
7-12 years of relevant experience in AI/ML system design and implementation.
Strong Python programming skills for building AI/ML and GenAI solutions.
Experience with Agent orchestration frameworks (LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen) and Vector databases (Pinecone, Milvus, Redis/pgvector).
Working knowledge of Amazon Bedrock AgentCore, AWS QuickSuite, AWS Transform, Kiro, plus expertise in NLP, CV, Deep Learning algorithms, SQL, and Open Source models.
Technical expert in large language models, multi-modal AI, and agentic AI architectures with hands-on system building experience.
Familiar with continuous integration and deployment processes, model monitoring including hallucination mitigation, and data observability.
Capable of collaborating with presales and customer teams to design solutions and stay updated with latest GenAI and AI/ML advancements.