Role Overview

We are seeking a highly skilled Senior AI/ML Engineer with extensive experience in document classification, natural language processing (NLP), and Large Language Models (LLMs). The ideal candidate will lead the development of our Document AI framework, specifically focusing on interpreting complex property and financial documents. You will be responsible for the end-to-end lifecycle of machine learning models, from fine-tuning LLMs for specialized extraction tasks to deploying scalable APIs in a production environment.

Key Responsibilities

• ● Model Development & Fine-tuning: Lead the selection, architecture, and fine-tuning of LLMs for high-accuracy document classification and data extraction from unstructured sources.

• ● End-to-End Extraction Pipelines: Design and optimize workflows to extract structured data from diverse documents such as GST certificates, Udyam registrations, FSSAI licenses, and property-related legal documents.

• ● Performance Evaluation: Conduct rigorous end-to-end analysis and result metric evaluation (e.g., Precision, Recall, F1-score) to ensure model reliability across production batches.

• ● OCR Integration: Collaborate with engineering teams to integrate central OCR services and refine extraction keys for specific business release cycles.

• ● Production Deployment: Build and maintain high-performance REST APIs and microservices using Python (FastAPI/Flask) within AWS environments.

• ● Advanced AI Tasks: Implement techniques for resolving name variations, identifying corporate hierarchies, and normalizing unstructured text from SMS and bureau records.

Required Skills & Qualifications

• ● Experience: 2-4 years of hands-on experience in AI/ML engineering, with a proven track record in document intelligence or specialized NLP.

• ● LLM Expertise: Demonstrated experience fine-tuning LLMs (e.g., Llama, Mistral, BERT-based architectures) for classification and information extraction tasks.

• ● Technical Stack: Strong proficiency in Python, including data handling libraries like Pandas and NumPy, and ML frameworks such as PyTorch or TensorFlow.

• ● Production Knowledge: Familiarity with CI/CD pipelines, MLOps, Docker, Kubernetes, and cloud platforms like AWS (specifically Lambda or EC2-based API deployments).

• ● Database Proficiency: Solid understanding of SQL (MySQL) and NoSQL databases for handling large-scale document metadata.

• ● Education: Bachelor’s or Master’s degree in Computer Science, AI, Mathematics, or a related field.