ML Engineer
CodeRound is hiring for this role.
- Machine Learning
- Machine Learning Engineer
- Deep Learning
- Computer Vision
- NLP
- Natural Language Processing
- Document AI
- Document Intelligence
- Intelligent Document Processing
- Python
- PyTorch
- Hugging Face Transformers
- Large Language Models
- LLMs
- OCR
- Information Extraction
- Semantic Search
- Multimodal AI
- FastAPI
- REST APIs
- AWS
- MLOps
- Model Deployment
- Neural Networks
- Data Science
We are looking for a Machine Learning Engineer with 3+ years of experience in building and deploying machine learning solutions, particularly in Computer Vision (CV), Natural Language Processing (NLP), and document intelligence. The role involves developing scalable ML models to process unstructured text and document data, automate workflows, and improve information extraction and content understanding. The ideal candidate combines strong Python and deep learning expertise with hands-on experience taking ML models from experimentation to production.
What you'll do
- Design, develop, and deploy ML models for document intelligence, content processing, and automated information extraction.
- Build end-to-end ML pipelines for processing unstructured text, images, scanned documents, and PDFs.
- Develop and improve NLP and CV solutions for document classification, entity extraction, semantic search, and document understanding.
- Experiment with deep learning architectures, Transformers, and LLM-based approaches to solve real-world document processing challenges.
- Convert research prototypes and experimental models into reliable, scalable, production-ready systems.
- Evaluate model performance using appropriate metrics, statistical analysis, benchmarking, and error analysis.
- Optimize model accuracy, inference speed, scalability, and resource utilization for production workloads.
- Build and maintain REST APIs and integrate ML models into existing product workflows.
- Collaborate with engineering and product teams to translate business requirements into effective ML solutions.
- Develop and maintain data pipelines, model documentation, experiment tracking, and deployment workflows.
- Monitor deployed models, identify performance issues, and continuously improve model quality and reliability.
- Stay up to date with advancements in machine learning, document AI, computer vision, NLP, and LLMs.
Must have
3+ years of hands-on experience in Machine Learning Engineering, with a strong focus on Computer Vision, NLP, or document intelligence. Strong proficiency in Python, with experience writing production-quality, maintainable code. Practical experience building, training, evaluating, and deploying machine learning and deep learning models. Strong understanding of deep learning architectures, including CNNs and Transformers, along with fundamental ML algorithms, mathematics, and statistics. Experience with PyTorch and the Hugging Face Transformers ecosystem. Hands-on experience with unstructured text and document data, including text classification, information extraction, document understanding, or semantic search. Experience building and maintaining production-grade REST APIs using frameworks such as FastAPI. Understanding of model evaluation, performance optimization, error analysis, and experimentation. Experience deploying ML models on cloud platforms such as AWS and working with production ML pipelines. Strong problem-solving skills and the ability to translate complex data challenges into practical ML solutions.
Good to have
Experience with Optical Character Recognition (OCR), document parsing, layout analysis, and intelligent document processing. Familiarity with Large Language Models (LLMs), embeddings, vector databases, and retrieval-augmented generation (RAG). Experience with multimodal models that combine text, images, and structured document layouts. Knowledge of object detection, image segmentation, and image classification techniques. Experience with MLOps practices, including model monitoring, versioning, CI/CD, and automated retraining pipelines. Familiarity with distributed processing and scalable data pipelines for large document volumes. Experience optimizing inference latency, throughput, memory utilization, and deployment costs. Exposure to containerization and orchestration tools such as Docker and Kubernetes. Experience working with scanned documents, PDFs, contracts, legal documents, or other complex enterprise content. Contributions to applied ML projects, research, or open-source libraries related to NLP, CV, or document AI.