
Machine Learning Solutions Architect
2 weeks ago
Advanced Machine Learning Engineer Role Overview
Job Description:We are seeking a seasoned expert to spearhead the development of cutting-edge machine learning models.
In this pivotal role, you will bridge the gap between Generative AI and MLOps, collaborating with researchers and engineers to transform research into production-ready features. Your expertise in inference optimization and automated workflows will enable seamless handovers from research to production.
You will be responsible for designing and maintaining production pipelines for diffusion-based and related generative models. Your skills in quantization, distillation, caching, and multi-GPU parallelism will ensure efficient model performance. Additionally, you will establish scalable data pipelines and storage solutions to support high-throughput workloads.
- Key Responsibilities:
- Collaborate with researchers to bring new models from prototype to production.
- Develop and maintain automated MLOps pipelines covering training, deployment, monitoring, and retraining.
- Optimize inference for speed, reliability, and efficiency using techniques like quantisation, distillation, caching, and multi-GPU parallelism.
- Create CI/CD workflows for machine learning that make handovers from research to production seamless and safe.
- Design secure, reliable infrastructure on AWS (S3, EC2, SageMaker) using Infrastructure-as-Code tools like Terraform.
Requirements:
- Generative AI Experience: Deployed diffusion-based or similar generative models into production and worked with inference optimization techniques.
- MLOps Expertise: Skilled in building automated, reliable workflows for training, deploying, and monitoring ML models at scale.
- Infrastructure & Cloud: Hands-on with AWS (S3, EC2, SageMaker), Kubernetes, Docker, and Infrastructure-as-Code (Terraform).
- Data Foundations: Ability to design scalable data pipelines and storage solutions (SQL/NoSQL).
- Performance & Efficiency: Proficient in techniques such as quantisation, distillation, caching, and distributed inference.
- Engineering Craft: Strong Python skills and a focus on writing clear, maintainable, and collaborative code.
- Collaboration & Growth: Thrive in cross-functional teams, value open feedback, and enjoy supporting others' success while learning continuously.
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