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Tier-1 employer, mid-level GenAI role, popular tech stack, and metro location.
Specialized GenAI, LLM fine-tuning and RAG experience leads to high domain specificity.
Explicit years plus mandatory GenAI/LLM skills and vector DB experience.
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Design and implement Retrieval-Augmented Generation (RAG) solutions to improve large language model (LLM) capabilities.
Develop and optimize LLM fine-tuning strategies for domain-specific uses, particularly in Wireless/5G.
Build and maintain autonomous multi-step AI agentic workflows and create evaluation frameworks for model performance.
Bachelor's degree in Engineering, Information Systems, Computer Science, or related field with 3+ years experience, OR Master's with 2+ years, OR PhD with 1+ year relevant experience.
1-5 years experience in AI/Machine Learning engineering.
Proficiency in Python and frameworks such as PyTorch or TensorFlow; experience with vector databases like Milvus or Pinecone.
Experience with LangChain, LlamaIndex, Transformers; knowledge of prompt engineering and reinforcement learning is highly valued.
Experienced in building scalable AI infrastructure integrating on-premises and cloud-based systems for real-time and high-throughput applications.
Skilled in advanced AI model development including RAG, fine-tuning, and agentic AI system workflows.
Background in software engineering within AI/ML domains, with a focus on applying models to domain-specific problems like Wireless/5G.