AI Engineer — LLM / VLM

Role Overview We are looking for an AI Engineer specializing in Large Language Models (LLMs) and Vision-Language Models (VLMs) to design, develop, and deploy production-grade AI solutions. The ideal candidate should have strong experience with LLM/VLM architectures, prompt engineering, RAG, fine-tuning, multimodal AI, and model serving. Key Responsibilities - Develop and deploy AI applications using LLMs and VLMs. - Build RAG pipelines involving document ingestion, chunking, embeddings, retrieval, reranking, and generation. - Work with models such as GPT, Claude, Gemini, Llama, Mistral, Qwen, and multimodal/VLM models. - Develop multimodal solutions involving text, images, PDFs, charts, tables, and documents. - Perform prompt engineering, supervised fine-tuning, LoRA/QLoRA, and model evaluation. - Build AI agents and tool-calling workflows where appropriate. - Optimize inference for latency, throughput, memory, and cost. - Develop APIs and production services using Python, FastAPI, Docker, and cloud platforms. - Implement evaluation frameworks to measure accuracy, hallucination, relevance, latency, and safety. - Collaborate with ML engineers, software engineers, and product teams to take prototypes into production. Required Skills - Strong Python programming and software-engineering fundamentals. - Hands-on experience with LLMs and/or VLMs. - Strong understanding of Transformers, attention mechanisms, tokenization, embeddings, and inference. - Experience with PyTorch and Hugging Face Transformers. - Experience building RAG systems and vector-search solutions. - Knowledge of prompt engineering and LLM evaluation. - Experience with APIs, REST services, Git, Docker, and CI/CD. - Familiarity with vector databases such as FAISS, Milvus, Pinecone, Weaviate, or pgvector. - Understanding of cloud AI infrastructure, preferably AWS/Azure/GCP. VLM / Computer Vision Skills - Experience with multimodal models such as Qwen-VL, LLaVA, Gemini, GPT vision models, or similar. - Understanding of image preprocessing and document/image understanding. - Experience with OCR, document intelligence, image classification, object detection, or visual question answering is a plus. - Ability to build pipelines combining vision + language + retrieval. Originally posted on Himalayas