Machine Learning Engineer - Document Intelligence Platform

We're looking for a Machine Learning Engineer to lead the design, development, and deployment of AI-powered systems. This role combines hands-on ML engineering, backend development, LLM integration, and production-grade infrastructure.The candidate will have responsibilities across the following functions :Document Intelligence :- Build AI systems for OCR, document classification, information extraction, and document understanding.- Develop workflows that convert unstructured real-estate documents into structured, actionable data.- Improve extraction accuracy through better data preparation, prompting, model selection, validation, and evaluation.- Collaborate with product and operations teams to translate business and compliance requirements into ML solutions.Backend engineering :- Build scalable backend services and APIs using Python and frameworks such as FastAPI or Flask.- Design REST APIs and integrations for AI, voice, and document-processing workflows.- Implement authentication, rate limiting, retries, queuing, and failure handling.- Debug, profile, and optimize API performance in production.- Write clean, maintainable, well-tested code and contribute to engineering standards.Infrastructure and MLOps :- Containerize applications and services using Docker.- Deploy and operate ML and backend services on Kubernetes or managed cloud platforms.- Contribute to CI/CD pipelines, automated testing, and safe release processes.- Implement logging, monitoring, alerting, and service-level metrics for AI systems.- Help manage model and service deployments across development, staging, and production environments.- Contribute to scalable, reliable, and cost-efficient cloud infrastructure.Collaboration and ownership :- Work closely with product, engineering, design, and operations teams to solve real business problems.- Convert ambiguous requirements into practical technical solutions.- Participate in architecture discussions and make thoughtful engineering trade-offs.- Provide technical guidance and mentorship to junior engineers.- Take ownership of projects from design and experimentation through deployment and ongoing improvement.Requirements :- 3 to 6 years of hands-on experience as an ML Engineer or similar role.- OCR & Multimodal AI : OCR, Vision-Language Models (VLMs), Intelligent Document Processing (IDP), Image Processing.- ML & Generative AI : NLP, Large Language Models (LLMs), LayoutLM, Document Classification, Entity Extraction.- Engineering & Backend : Python, FastAPI, Flask, RESTful APIs, Workflows, Queuing Systems.- MLOps & Infrastructure : Docker, Kubernetes, CI/CD, Model Monitoring, Cloud Platforms (AWS/GCP/Azure). (ref:hirist.tech)