Job Title

About the Role We are looking for a highly motivated AI & Machine Learning Engineer with strong expertise in modern AI technologies, including Large Language Models (LLMs). The role focuses on end-to-end AI and ML development, including designing, building, and deploying intelligent applications that leverage machine learning, LLMs, and retrieval-augmented generation (RAG) techniques.The ideal candidate demonstrates strong Python development skills, hands-on experience working with LLMs, prompt engineering, and RAG-based application development, along with solid analytical thinking and the ability to communicate complex concepts effectively to both technical teams and business stakeholders.Key Responsibilities AI / ML & Document Intelligence - Design and implement AI/ML solutions for document abstraction, classification, and information extraction from structured and unstructured documents (PDFs, scanned files, Office documents). - Develop and optimize NLP, OCR, and transformer-based models for entity extraction, classification, and summarization. - Build end-to-end document processing pipelines integrating OCR, ML inference, and post-processing rules. - Continuously evaluate and improve model accuracy, performance, and reliability.Software Engineering - Develop clean, efficient, and scalable Python code following engineering best practices. - Build and maintain RESTful APIs and microservices (e.g., FastAPI or similar frameworks). - Ensure code quality through unit testing, logging, error handling, and observability. - Collaborate with frontend teams (Angular/SPA or equivalent) for AI service integration. Analytical & Problem Solving - Analyze complex document layouts, content variability, and edge cases. - Perform data exploration, feature engineering, and root-cause analysis of model failures. - Translate business requirements and document rules into effective ML-based solutions. Collaboration & Communication - Work closely with product managers, architects, data engineers, and business stakeholders. - Clearly explain AI model behavior, outputs, trade-offs, and limitations to non-technical audiences. - Contribute to design reviews, sprint planning, demos, and technical documentation.Required Qualifications Technical Skills - Strong proficiency in Python for data processing, ML development, and API building. - Strong knowledge and hands-on experience with Large Language Models (LLMs). - Experience building applications using Retrieval-Augmented Generation (RAG) patterns. - Hands-on experience with prompt engineering and optimizing LLM outputs. - Solid understanding of Machine Learning, NLP, and information extraction techniques. - Hands-on experience with ML frameworks and libraries such as PyTorch, TensorFlow, scikit-learn, spaCy, Transformers, or equivalent. - Experience working with OCR and document AI workflows. - Familiarity with cloud-native development and REST-based architectures. - Working knowledge of databases (SQL/NoSQL) and data pipelines.Analytical Skills - Strong analytical thinking and structured problem-solving ability. - Ability to understand and model complex document structures and patterns. - Experience measuring model performance using both technical and business-oriented metrics.Communication & Professional Skills - Strong verbal and written communication skills. - Ability to collaborate effectively in cross-functional teams. - Comfortable presenting technical ideas and AI outcomes to diverse audiences.Preferred / Nice to Have - Experience with cloud AI platforms (Azure, AWS, or GCP). - Exposure to MLOps, model deployment, monitoring, and lifecycle management. - Familiarity with vector databases and embedding-based search. - Familiarity with security, authentication, and enterprise data governance standards. - Experience working in Agile / Scrum environments.