Website:
manthhan.com
Job details:
Role Summary:
We are seeking a Machine Learning Engineer to design, develop, deploy, and optimize machine learning and AI solutions for real-world business applications. The ideal candidate will have hands-on experience in building scalable ML systems, deploying production-grade AI models, and working with advanced AI technologies including Optical Character Recognition (OCR), Natural Language Processing (NLP), and Generative AI solutions.
Key Responsibilities:
- Train, fine-tune, evaluate, and deploy machine learning models like LLM, SLM, VLM into production environments.
- Design, develop, and deploy agentic AI workflows
- Design and maintain scalable ML pipelines and model-serving infrastructure.
- Build and optimize OCR-based solutions for document digitization, information extraction, and intelligent document processing.
- Develop AI solutions using NLP, Computer Vision, OCR, and Generative AI.
- Collaborate with Data Scientists and Software Engineers to operationalize AI models.
- Monitor model performance and implement continuous learning pipelines.
- Optimize algorithms for efficiency, scalability (distributed training, fine-tuning and inference), and accuracy.
- Implement ML Ops best practices and CI/CD pipelines.
- Ensure reliability, security, and compliance of deployed AI systems.
- Maintain technical documentation and deployment workflows.
Requirements:
- Bachelor's or master's degree or PhD in computer science, Artificial Intelligence, Machine Learning, or related fields.
- 3–8 years of experience in Machine Learning Engineering or AI development.
- Practical understanding and using of RAG concepts, non-RAG based methods, and transformer-based neural network architectures.
- Mathematical foundations and principles of ML, DL and GenAI
- Strong proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Hands-on experience with OCR technologies such as Tesseract, PaddleOCR, EasyOCR, AWS tesseract, Azure Document Intelligence, or Google Vision AI.
- Experience working with NLP, Computer Vision, and Deep Learning models.
- Familiarity with cloud platforms (AWS, Azure, GCP) and containerization technologies.
- Understanding of MLOps, model deployment, and automation practices.
- Knowledge of data structures, algorithms, and software engineering principles.
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