Artificial Intelligence Engineer

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Role Overview

The AI/ML Engineer will design, build, and productionize machine learning models end-to-end. The role focuses on developing robust models, deploying them to production, and exposing them via REST APIs for integration with products and services. You will collaborate with software engineers and product teams to ensure scalable, reliable, and secure inference services. Experience with NLP and deep learning is beneficial, as is hands-on use of cloud ML platforms such as AWS SageMaker or Azure AI/ML.

Required Qualifications

• Proven experience (5+ years) in end-to-end ML model development (problem framing, data prep, training, evaluation) [Mandatory]
• Hands-on model deployment to production environments (on-prem or cloud) [Mandatory]
• REST API integration for ML inference (building/consuming model endpoints) [Mandatory]
• Experience with cloud ML platforms like AWS SageMaker or Azure AI/ML [Preferred]
• Exposure to NLP techniques and workflows [Preferred]
• Experience with deep learning model development and serving [Preferred]

Responsibilities

• Design, train, evaluate, and iterate ML models aligned to product or business objectives.
• Deploy ML models to production and expose them as scalable REST endpoints for consumption by applications and services.
• Integrate model services with existing systems, ensuring reliability, low latency, and secure access.
• Collaborate with software engineers, data engineers, and product stakeholders through the model lifecycle.
• Leverage cloud ML platforms (e.g., AWS SageMaker, Azure AI/ML) for training, deployment, and management when applicable.
• Monitor, troubleshoot, and improve models and inference services post-deployment (performance, accuracy, and uptime).