Remote · worldwide, UTC+4 acceptedSalary not disclosedfull-timeVerified 1w agoHimalayas
We are seeking a talented Machine Learning Engineer II to join our CAI machine learning and scoring development team. In this role, you will be the crucial bridge between applied research and production systems.
Responsibilities
Full-Lifecycle ML Development
Lead the transition of machine learning models from theoretical prototypes into scalable, high-performance production systems.
AWS Cloud Architecture & Deployment: Architect and deploy ML solutions utilizing AWS ECS (Elastic Container Service) for containerized workloads and AWS Lambda for serverless, event-driven inference pipelines.
Model & Inference Optimization: Optimize PyTorch models for production deployment by converting them to ONNX formats. Apply advanced inference optimization techniques (quantization, pruning, ONNX Runtime) and memory-efficient attention mechanisms like Flash Attention to minimize latency and maximize throughput.
Infrastructure & Engineering Best Practices
Champion infrastructure best practices for machine learning systems, establishing reliable CI/CD pipelines, and ensuring robust, secure, and reproducible deployments across the AWS ecosystem.
Algorithm Engineering
Design, develop, and evaluate algorithms that generate descriptive, diagnostic, predictive, and prescriptive insights from both structured and unstructured data.
Robust Software Engineering: Write clean, efficient, and well-tested code. Complete rigorous testing, debugging, and documentation to ensure seamless installation and long-term maintenance.
Cross-Functional Collaboration: Actively participate in research discussions, requirements gathering, and system design alongside domain experts to build tailored scoring and ML solutions.
