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 .