AI ML Engineer
Qode
Job Description
AI ML Engineer We are seeking a Senior AI/ML Engineer with 5+ years of hands-on experience in Artificial Intelligence and Machine Learning. The ideal candidate should have worked in organizations where AI/ML technologies are actively implemented in production environments, particularly in Document AI and intelligent automation. Key Responsibilities Design, develop, and deploy scalable AI/ML models for document processing and automation.
Build end-to-end pipelines for data extraction, classification, and validation. Implement and integrate Document AI and OCR-based solutions. Develop LLM-driven applications using modern frameworks and orchestration tools.
Collaborate with cross-functional teams to deliver business-focused AI solutions. Optimize model performance, accuracy, and scalability in production environments. Stay updated with the latest advancements in AI/ML technologies.
Core Competencies Strong experience in production-grade AI/ML implementations. Expertise in Intelligent Document Processing (IDP) and Document AI. Hands-on experience with LLM frameworks and workflow orchestration.
Strong analytical, problem-solving, and debugging skills. Ability to work in fast-paced, technology-driven environments. Must-Have Technical Skills Rossum Hyperscience Nanonets Infrrd Microsoft Azure AI Document Intelligence AWS Textract LandingAI ADE Split LlamaParse LayoutLMv3 Donut (Document Understanding Transformer) LangGraph LangChain Education & Qualifications Bachelor’s or Master’s degree in Artificial Intelligence, Machine Learning, Computer Science, or a related field.
Additional Requirement Candidates must have prior experience working with organizations that actively utilize AI/ML technologies in real-world, production-grade environments. Strong Mathematical foundation (Linear Algebra, Probability, Statistics, Optimization) is highly desirable. Preferred Qualifications Experience in large-scale AI deployments on cloud platforms.
Strong background in NLP, OCR, and document automation workflows. Experience with model fine-tuning, prompt engineering, and evaluation techniques.