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 docume