At RemoteStar, we're currently hiring for one of our client based in Spain About client : Well-funded and fast-growing deep-tech company founded in 2019. We are the biggest Quantum Software company in the EU. They are also one of the 100 most promising companies in AI in the world (according to CB Insights, 2023) with 150 employees and growing, fully multicultural and international. Required Qualification 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 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. Preferred Qualifications Experience in training “Mixture-of-Experts" Experience working with different public cloud providers and hybrid environments. Experience in real-time streaming applications. Experience with training pipeline optimization, inference optimization, LLM observability and LLM API management As a MLOps Engineer, you will: 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. We offer Competitive annual salary starting from €55,000, based on experience and qualifications. Two unique bonuses: signing bonus at incorporation and retention bonus at contract completion. Relocation package (if applicable). Up to 9-month contract, ending on June 2026. Hybrid role and flexible working hours. Be part of a fast-scaling Series B company at the forefront of deep tech. Equal pay guaranteed. International exposure in a multicultural, cutting-edge environment.