Machine learning engineer

Build, manage, and optimize ML/LLM training and deployment pipelines ensuring seamless transition from development to production. Deploy and maintain models and LLM-powered applications in production environments with high reliability and scalability. Manage model versioning and approval workflows to support continuous integration and deployment cycles. Optimize LLM inference performance through techniques such as quantization, batching, and model routing to improve latency, throughput, and cost-efficiency. Configure and manage endpoints, scaling policies, and deployment strategies including blue-green and shadow deployments. Deploy and manage advanced AI systems such as AI agents, Retrieval-Augmented Generation (RAG) systems, and LLM APIs where applicable. Monitor production performance, reliability, and operational costs using observability tools, providing actionable insights to improve system robustness. Provide detailed, constructive feedback on SageMaker workflows, failure modes, and operational tooling to enhance platform capabilities. Evaluate and benchmark platform capabilities against competing, cloud-based, and open-source ML solutions to ensure best-in-class infrastructure.