MLOps Engineer, Multiverse Computing

• Bachelor's or master's degree in computer science, Engineering, or a related field
• Mid - 2+ years of experience as an ML/LLM engineer in public cloud platforms
• Senior - 5+ years of experience as an ML/LLM engineer in public cloud platforms
• Proven experience in MLOps, LLMOps, or related roles, with hands-on experience in managing machine/deep learning and large language model pipelines from development to deployment and monitoring
• Expertise in cloud platforms (e.g., AWS, Azure) for ML workloads, MLOps, DevOps, or Data Engineering
• Expertise in model parallelism in model training and serving, and data parallelism/hyperparameter tuning
• Proficiency in programming languages such as Python, distributed computing tools such as Ray, model parallelism frameworks such as DeepSpeed, Fully Sharded Data Parallel (FSDP), or Megatron LM
• Expertise in with generative AI applications and domains, including content creation, data augmentation, and style transfer
• Strong understanding of Generative AI architectures and methods, such as chunking, vectorization, context-based retrieval and search, and working with Large Language Models like OpenAI GPT 3.5/4.0, Llama2, Llama3, Mistral, etc
• Experience with Azure Machine Learning, Azure Kubernetes Service, Azure CycleCloud, Azure Managed Lustre
• Experience with Perfect English, Spanish is a plus
• Great communication skills and a passion for working collaboratively in an international environment

Desirable

• You’ll be working alongside world-leading experts to develop solutions that tackle real-world challenges. We’re looking for passionate individuals eager to grow in an ethics-driven environment that values sustainability and diversity
• Deploy cutting-edge ML/LLMs models to Fortune Global 500 clients
• Join a world-class team of Quantum experts with an extensive track record in both academia and industry
• Collaborate with the founding team in a fast-paced startup environment
• Design, develop, and implement Machine Learning (ML) and Large Language Model (LLM) pipelines, encompassing data acquisition, preprocessing, model training and tuning, deployment, and monitoring
• Employ automation tools such as GitOps, CI/CD pipelines, and containerization technologies (Docker, Kubernetes) to enhance ML/LLM processes throughout the Large Language Model lifecycle
• Establish and maintain comprehensive monitoring and alerting systems to track Large Language Model performance, detect data drift, and monitor key metrics, proactively addressing any issues
• Conduct truth analysis to evaluate the accuracy and effectiveness of Large Language Model outputs against known, accurate data
• Collaborate closely with Product and DevOps teams and Generative AI researchers to optimize model performance and resource utilization
• Manage and maintain cloud infrastructure (e.g., AWS, Azure) for Large Language Model workloads, ensuring both cost-efficiency and scalability
• Stay updated with the latest developments in ML/LLM Ops, integrating these advancements into generative AI platforms and processes
• Communicate effectively with both technical and non-technical stakeholders, providing updates on Large Language Model performance and status