NLP & Image Processing Engineer

চাকরির বিবরণ

You will develop real-world NLP and image processing solutions — from OCR pipelines and document intelligence to multi-modal AI systems that understand both text and visual content in production environments. WorkSprout is a deep-tech engineering and design partner across twelve practice areas — TinyML & Edge AI, Custom AI Development, AI Automation & Agents, Data Engineering & Collection, IoT Solutions, Hardware Prototyping, Robotics & Automation, 3D Design & Modeling, Software Development, Growth & DevOps, Branding & Creative Design, and Startup & Product Launch. We deliver systems that work in production, not just in demos. Office hours 09:00 – 18:00 BST, Monday to Friday; hybrid in Dhaka, Bangladesh. Applications are reviewed on a rolling basis. To apply, email careers@worksprout.us with the subject "NLP & Image Processing Engineer".

দায়িত্ব

• Build and fine-tune NLP models for text classification, extraction, and summarisation
• Develop image processing pipelines using OpenCV and deep learning frameworks
• Implement multi-modal models combining text and vision understanding
• Create OCR and document intelligence systems for structured data extraction
• Deploy models as low-latency microservices with REST interfaces
• Evaluate model performance against real-world accuracy and recall benchmarks

যোগ্যতা

• Strong Python with PyTorch or TensorFlow
• Experience with Hugging Face Transformers and pre-trained NLP models
• Proficiency in OpenCV and image processing fundamentals
• Understanding of CNN, ViT, and multi-modal architectures
• Experience deploying ML models to production (ONNX, TorchServe, FastAPI)
• Nice to have: Experience with Bengali or multilingual NLP pipelines
• Nice to have: Knowledge of Tesseract OCR and document layout analysis
• Nice to have: Familiarity with CLIP, BLIP, or LLaVA for vision-language tasks

সুবিধা

• Work on diverse real-world language and vision problems
• Competitive salary benchmarked to international rates
• Flexible hybrid work arrangement
• GPU compute access for training and experimentation
• Dedicated research time for paper reading and prototyping