AI/ML Engineer - Document Intelligence
First seen by Alion on Nov 4, 2026.
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: BE/Btech in Computer Science from Tier1 colleges.
Skills
Machine Learning, Python, Generative AI, NLP, FastAPI, PyTorch, Tensorflow, AWS, MLOps