About the Role

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 protected 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.

● Expert-level Python programming and clean code practices.

● Strong experience designing and integrating production APIs.

● Practical experience integrating LLM models and writing optimised prompts.

● Strong understanding of model fine-tuning, hyperparameter tuning, and inference optimization.

● Hands-on expertise in large-scale document processing and classification, with healthcare document workflow experience being a strong plus.

● Experience with Docker, containerised deployments, and Kubernetes orchestration.

● Good understanding of microservices architecture, distributed systems, and cloud infrastructure.

● Solid problem-solving and debugging skills across the ML lifecycle.

Nice-to-Have:

● Experience with vector databases (Pinecone, Weaviate, FAISS).

● Experience with event-driven architecture (Kafka, Pub/Sub, SQS/SNS).

● Exposure to data pipelines (Airflow, Prefect, Dagster).

📌 ML Engineer (Bengaluru)
🏢 ENTER
📍 Bengaluru