Machine Learning Engineer

We are seeking a highly motivated ML Engineer & Developer with 4–5 years of experience in core machine learning and deep learning. The ideal candidate will have strong expertise in statistical analysis, NLP, and Computer Vision , along with hands-on experience in transformer -based models and OCR systems. This role also involves contributing to R&D initiatives and mentoring junior team members while staying up to date with the latest advancements in AI.\n\nKey Responsibilities:\nDesign, develop , and deploy machine learning and deep learning models for real-world applications\nPerform statistical analysis and data exploration to derive meaningful insights ( Time series analysis & Forecasting, predictive analysis )\nBuild and optimize NLP and Computer Vision pipelines for various use cases\nFine-tune transformer-based models for tasks such as text classification, entity recognition, and vision tasks (BERT, LayoutLMv3, Vision Transformers,etc)\nBuild document AI systems (text + layout + image)\nPerform feature engineering and EDA\nWork on domain-specific keyword extraction systems\nContribute to R&D in NLP, OCR, and multimodal AI\nOptimize models for accuracy, scalability, and performance\nCollaborate with cross-functional teams to integrate ML models into production systems\nContinuously evaluate and adopt new AI/ML techniques, tools, and frameworks\nMentor and guide junior engineers, helping them understand core concepts and best practices\nMaintain documentation and ensure knowledge sharing within the team\n\nRequired Skills & Experience:\n4–5 years of hands-on experience in AI/ML development\nStrong foundation in ML, Deep Learning, Statistics & Mathematics\nExpertise in NLP + Computer Vision\nHands-on with: Transformer models (e.g., BERT, Vision Transformers) and fine-tuning techniques\nProficiency in Python and ML/DL frameworks such as TensorFlow /PyTorch\nExperience in building end-to-end ML pipelines and Strong algorithmic thinking\nUnderstanding of data preprocessing, feature engineering, and model evaluation techniques\nStrong problem-solving skills and ability to work on R&D-focused tasks\n\nNice to Have:\nExperience with Generative AI and deep learning architectures ( LLMs, diffusion models, etc.)\nModel optimization ( quantization, pruning)\nFamiliarity with MLOps practices and model deployment workflows\nExperience with cloud platforms ( Azure, AWS, or GCP)\nKnowledge of vector databases and embedding techniques\nExperience with annotation tools and dataset creation for OCR/NLP tasks\nExposure to model optimization techniques (quantization, pruning, distillation)