ML Engineer

Design and implement LLM-based pipelines for tasks such as document summarization, code generation,
and knowledge
management.
" Develop prompt engineering and few-shot learning strategies to adapt LLMs for domain-specific
applications.
" Build and deploy multi-agent AI systems integrating LLMs with APIs and external tools for
autonomous task execution.
" Enhance LLM accuracy using retrieval-augmented generation (RAG) with vector databases (FAISS,
Pinecone, Milvus).
" Apply model alignment methods such as reinforcement learning with human feedback (RLHF) to ensure
reliability and safety.
" Optimize LLM inference with quantization, pruning, LoRA, and distributed GPU deployment to reduce
cost and latency.
" Integrate multimodal LLMs (text + image) for use cases like content moderation, product search,
and digital assistants.