ML Engineer

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 optimise API performance in production.
• Write clean, maintainable, well-tested code and contribute to engineering standards.

Infrastructure and MLOps:

• Containerise 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.