Cloud MLOps Engineer supporting Data Science and Engineering teams by automating CI/CD pipelines and managing multi - cloud infrastructure for ML production.
About the role
- MLOps Engineer at a fast-growing Quantum Software company deploying ML/LLM models for Fortune Global 500 clients. Collaborating with experts to design, develop, and implement large-scale ML solutions.
Responsibilities
- 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.
Requirements
- Bachelor's or master's degree in computer science, Engineering, or a related field.
- Mid or Senior: 4+ 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 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.
Benefits
- Two unique bonuses: signing bonus at incorporation and retention bonus at contract completion.
- Relocation package (if applicable).
- Hybrid role and flexible working hours.
- Equal pay guaranteed.
- International exposure in a multicultural, cutting-edge environment.
Job title
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