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.

Mandatory Key Skillskubernetes,docker,microsoft azure,gcp,aws sagemaker,azure devops,linux,google,ai platform,machine learning,ml,artificial intelligence,java,natural language processing,python*,sql*,linux internals*,cloud infrastructure management*,aws*