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Niche agentic AI skillset and senior level reduce applicants despite metro location and recognizable employer.
Highly specialized ML/LLM, vector DB, and agent orchestration skills limit cross-industry transferability.
Explicit 7–12 years and many mandatory ML/LLM/vectorization technologies required.
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Design and build Agentic AI systems including chat copilots, workflow/graph agents, and related tools focusing on GenAI technologies.
Implement and optimize advanced AI features such as chunking, hybrid search, vector stores, re-ranking, and continuous data quality evaluation.
Oversee model selection, fine-tuning, monitoring, and address challenges like hallucinations while collaborating with presales and customer teams for solution delivery.
7-12 years of experience in machine learning, AI, or related fields.
Strong proficiency in Python and hands-on experience with Amazon Bedrock AgentCore, AWS QuickSuite, AWS Transform, and Kiro.
Experience with Agent orchestration frameworks including LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen and working knowledge of Vector databases like Pinecone, Milvus, Redis/pgvector.
Knowledge of CI/CD processes, guardrails configuration, PII redaction, LLM models monitoring, fine-tuning, and experience in NLP, Deep Learning, CV, SQL query and Open Source Models.
Experienced technical architect with deep expertise in designing and operationalizing Agentic AI systems at scale using GenAI and LLMs.
Technically versatile professional skilled in integrating multiple AI/ML technologies and frameworks to build innovative and production-ready solutions.
Strategic thinker who collaborates effectively with presales and customer teams and stays updated on state-of-the-art AI/ML advancements for continuous solution improvement.