AI Engineer

Responsibilities

• Design, develop, and deploy end-to-end machine learning pipelines in cloud-native environments, ensuring scalability and reliability.
• Collaborate with data scientists to productionalize ML models, transitioning from prototype to enterprise-ready solutions.
• Optimize cloud-based data architectures and ML systems for high performance and cost efficiency (AWS, Azure, GCP).
• Integrate ML models into existing and new system architectures, designing robust APIs for seamless model consumption.
• Implement MLOps and LLMOps best practices, including CI/CD for ML models, monitoring, and automated retraining workflows.
• Continuously assess and improve ML system performance, ensuring high availability and minimal downtime.
• Stay ahead of AI and cloud trends, collaborating with cloud architects to leverage cutting-edge technologies

Qualifications

• 4+ years of experience in cloud-native ML engineering, deploying and maintaining AI models at scale.
• Hands-on experience with AI cloud platforms (Azure ML, Google AI Platform, AWS SageMaker) and cloud-native services.
• Strong programming skills in Python and SQL, with expertise in cloud-native tools like Kubernetes and Docker.
• Experience building automated ML pipelines, including data preprocessing, model deployment, and monitoring.
• Proficiency in Linux environments and cloud infrastructure management.
• Experience operationalizing GenAI applications or AI assistants is a plus.
• Strong problem-solving, organizational, and communication skills.