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Specialized LLM/agent skills reduce pool, but AI engineer title and early-mid seniority increase applicant density.
Requires specialized LLM, vector DB, and MLOps skills limiting cross-industry transfer.
Explicit 2–3 years plus mandatory LLM, vector DB, and MLOps skills make filters strict.
Design, develop, deploy, and maintain AI and machine learning solutions addressing business challenges.
Translate client use cases into scalable AI solutions including traditional ML, GenAI, and Retrieval-Augmented Generation (RAG) architectures.
Architect and deploy autonomous multi-agent AI systems enabling complex reasoning, decision making, task routing, and integration with external tools and APIs.
2 to 3 years experience in AI solution development or machine learning engineering.
Proven track record taking AI/ML models from concept to production.
Proficiency in Python; familiarity with Java and Angular/React for full-stack integration.
Hands-on experience with agentic AI frameworks (LangGraph, CrewAI, AutoGen) and LLM orchestration (LangChain, LlamaIndex).
Strong expertise in designing autonomous AI systems with multi-agent orchestration and advanced reasoning capabilities.
Experience integrating large language models (OpenAI, Google Gemini, Claude) with vector databases and building Retrieval-Augmented Generation pipelines.
Operational knowledge of MLOps practices including containerization, cloud provisioning, CI/CD pipeline development, and infrastructure as code.