AI Engineer — LLM / VLM

Accountabilities:

• Design, develop, and deploy production-ready AI applications powered by Large Language Models and Vision-Language Models, selecting appropriate models and architectures based on business and technical requirements.
• Build end-to-end RAG pipelines covering document ingestion, chunking, embedding generation, retrieval, reranking, and response generation, with a focus on accuracy, relevance, scalability, and reliability.
• Work with leading foundation models and multimodal models, including GPT, Claude, Gemini, Llama, Mistral, Qwen, and comparable technologies, evaluating their suitability for different use cases.
• Develop multimodal AI solutions that can process and reason across text, images, PDFs, charts, tables, and complex business documents.
• Apply prompt engineering, supervised fine-tuning, LoRA/QLoRA, and other model adaptation techniques to improve model performance for specific applications.
• Build AI agents and tool-calling workflows where they provide meaningful value, designing systems that can combine reasoning, retrieval, external tools, and business processes.
• Optimize model inference and serving for latency, throughput, memory consumption, scalability, and cost, balancing technical performance with production requirements.
• Develop production APIs and AI services using Python, FastAPI, Docker, and cloud infrastructure, while applying sound software engineering, version control, testing, and deployment practices.
• Create evaluation frameworks and metrics to measure AI system accuracy, relevance, hallucination rates, latency, safety, and overall production quality.
• Collaborate with ML engineers, software engineers, and product teams to transform experimental AI concepts into robust, maintainable, and production-ready solutions.

Requirements

• Bring strong Python programming skills and solid software engineering fundamentals, with the ability to build clean, maintainable, and production-ready AI applications.
• Have hands-on professional experience working with Large Language Models and/or Vision-Language Models and a strong understanding of how modern generative AI systems are developed and deployed.
• Demonstrate a solid understanding of Transformers, attention mechanisms, tokenization, embeddings, model inference, and the underlying concepts that enable modern language and multimodal models.
• Have practical experience with PyTorch and Hugging Face Transformers, including working with or adapting open-source and foundation models.
• Bring hands-on experience designing and implementing RAG systems and vector-search solutions, including knowledge of retrieval and embedding strategies.
• Demonstrate knowledge of prompt engineering and LLM evaluation methodologies, with an understanding of how to assess model quality, hallucinations, relevance, and safety.
• Have experience developing APIs and REST services and working with Git, Docker, CI/CD, and modern software development practices.
• Be familiar with vector databases or vector-search technologies such as FAISS, Milvus, Pinecone, Weaviate, or pgvector.
• Understand cloud-based AI infrastructure and deployment, with experience in AWS, Azure, GCP, or comparable cloud environments.
• Have experience with multimodal models such as Qwen-VL, LLaVA, Gemini, GPT vision models, or similar technologies, along with an understanding of image preprocessing and document or image understanding.
• Experience with OCR, document intelligence, image classification, object detection, or visual question answering is a plus, as is the ability to build integrated pipelines combining vision, language, and retrieval.
• Bring strong analytical and problem-solving abilities, curiosity for emerging AI technologies, and the ability to collaborate effectively with technical and product stakeholders in an experienced engineering environment.

Benefits

• Remote work from India, with the role based in Kolkata and structured as a remote position.
• Full-time employment within a technology-focused environment working on advanced AI and generative AI applications.
• Opportunities to work hands-on with LLMs, VLMs, RAG, AI agents, multimodal AI, fine-tuning, and model serving.
• Exposure to leading AI technologies and models, including GPT, Claude, Gemini, Llama, Mistral, Qwen, and other emerging foundation models.
• The opportunity to build production-grade AI systems rather than limiting your work to experimentation and prototypes.
• Broad technical exposure across machine learning, software engineering, cloud infrastructure, APIs, vector databases, evaluation frameworks, and AI deployment.
• The opportunity to collaborate with ML engineering, software engineering, and product teams while contributing directly to the development of practical AI capabilities.