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.

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 .