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).