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:\n\n- Design, develop, and deploy machine learning and deep learning models for real-world applications\n- Perform statistical analysis and data exploration to derive meaningful insights (Time series analysis & Forecasting, predictive analysis)\n- Build and optimize NLP and Computer Vision pipelines for various use cases\n- Fine-tune transformer-based models for tasks such as text classification, entity recognition, and vision tasks (BERT, LayoutLMv3, Vision Transformers,etc)\n- Build document AI systems (text + layout + image)\n- Perform feature engineering and EDA\n- Work on domain-specific keyword extraction systems\n- Contribute to R&D in NLP, OCR, and multimodal AI\n- Optimize models for accuracy, scalability, and performance\n- Collaborate with cross-functional teams to integrate ML models into production systems\n- Continuously evaluate and adopt new AI/ML techniques, tools, and frameworks\n- Mentor and guide junior engineers, helping them understand core concepts and best practices\n- Maintain documentation and ensure knowledge sharing within the team\n\nRequired Skills & Experience:\n\n- 4–5 years of hands-on experience in AI/ML development\n- Strong foundation in ML, Deep Learning, Statistics & Mathematics\n- Expertise in NLP + Computer Vision\n- Hands-on with: Transformer models (e.g., BERT, Vision Transformers) and fine-tuning techniques\n- Proficiency in Python and ML/DL frameworks such as TensorFlow /PyTorch\n- Experience in building end-to-end ML pipelines andStrong algorithmic thinking\n- Understanding of data preprocessing, feature engineering, and model evaluation techniques\n- Strong problem-solving skills and ability to work on R&D-focused tasks\n\nNice to Have:\n\n- Experience with Generative AI and deep learning architectures (LLMs, diffusion models, etc.)\n- Model optimization (quantization, pruning)\n- Familiarity with MLOps practices and model deployment workflows\n- Experience with cloud platforms (Azure, AWS, or GCP)\n- Knowledge of vector databases and embedding techniques\n- Experience with annotation tools and dataset creation for OCR/NLP tasks\n- Exposure to model optimization techniques (quantization, pruning, distillation)