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Mid-level, metro AI engineer role with broad LLM/RAG skillset increases candidate competition.
LLM and ML-specific skills are transferable but require domain experience, causing medium sensitivity.
Explicit 2–4 years plus mandatory Python, LLM, prompt-engineering and cloud skills increases filtering.
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Build and deploy AI applications using large language models (LLMs) with focus on prompt engineering and real-world solutions.
Design and implement advanced prompt engineering techniques including few-shot, zero-shot, chain-of-thought prompting, and optimize prompt accuracy and evaluation.
Develop retrieval-augmented generation (RAG) solutions, integrate AI models into APIs and business workflows, and deliver prototypes/POCs within 2–4 weeks.
2–4 years of work experience in AI or related roles.
Strong Python programming skills mandatory.
Experience with LLMs, prompt engineering, embeddings, and retrieval-augmented generation (RAG).
Familiarity with ML basics such as model training and algorithms like Random Forest or regression; experience with backend/API integration; knowledge of Azure cloud platform preferred.
Experienced in hands-on prompt engineering and LLM-based AI application development with ability to optimize model outputs and control hallucinations.
Comfortable rapidly prototyping AI solutions and working with vector databases and tools like LangChain or LlamaIndex.
Capable of translating ambiguous business problems into practical AI solutions, owning end-to-end solution delivery, and balancing cost-performance of models.
