We are looking to fill this role immediately and are reviewing applications daily. Expect a fast, transparent process with quick feedback.
Why join us?
We are a European deep-tech leader in quantum and AI, backed by major global strategic investors and strong EU support. Our groundbreaking technology is already transforming how AI is deployed worldwide — compressing large language models by up to 95% without losing accuracy and cutting inference costs by 50–80%.
Joining us means working on cutting-edge solutions that make AI faster, greener, and more accessible — and being part of a company often described as a “quantum-AI unicorn in the making.”
We offer
- Competitive annual base salary, based on experience and qualifications.
- Two unique bonuses: signing bonus at incorporation and retention bonus at contract completion.
- Relocation package (if applicable).
- Fixed-term contract ending in 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.
- As a MLOps/LLMOps 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.
Required Qualification
- Bachelor's or master's degree in computer science, Engineering, or a related field.
- 1+ 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.
- Experience in cloud platforms (e.g., AWS, Azure) for ML workloads, MLOps, DevOps, or Data Engineering.
- Knowledge 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.
- Knowledge 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.
About Multiverse Computing
Founded in 2019, we are a well-funded, fast-growing deep-tech company with a team of 180+ employees worldwide. Recognized by CB Insights (2023 & 2025) as one of the Top 100 most promising AI companies globally, we are also the largest quantum software company in the EU.
Our flagship products address critical industry needs:
- CompactifAI → a groundbreaking compression tool for foundational AI models, reducing their size by up to 95% while maintaining accuracy, enabling portability across devices from cloud to mobile and beyond.
- Singularity → a quantum and quantum-inspired optimization platform used by blue-chip companies in finance, energy, and manufacturing to solve complex challenges with immediate performance gains.
You’ll be working alongside world-leading experts in quantum computing and AI, developing solutions that deliver real-world impact for global clients. We are committed to an inclusive, ethics-driven culture that values sustainability, diversity, and collaboration — a place where passionate people can grow and thrive. Come and join us!
As an equal opportunity employer, Multiverse Computing is committed to building an inclusive workplace. The company welcomes people from all different backgrounds, including age, citizenship, ethnic and racial origins, gender identities, individuals with disabilities, marital status, religions and ideologies, and sexual orientations to apply.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The MLOps/LLMOps Engineer function is structurally essential for translating theoretical quantum-inspired AI optimizations into scalable, reliable enterprise solutions. This role addresses the critical challenge of productionizing complex hybrid models, bridging the gap between research environments and client-facing cloud infrastructure. The necessity for robust MLOps practices is heightened by the computational intensity and unique constraints associated with deploying compressed Large Language Models (LLMs) and quantum-derived algorithms at scale, directly impacting resource utilization and total cost of ownership across diverse computing substrates. This specialization mitigates integration friction and accelerates the adoption lifecycle of advanced generative AI technology in regulated industrial sectors.
The intersection of quantum computing and advanced artificial intelligence currently occupies a critical position within the global deep-tech value chain, focusing heavily on application enablement and optimization platforms. Sector analysis by organizations like the OECD and McKinsey identifies operational maturity (TRL 7-9) in software delivery pipelines as a primary bottleneck for enterprise adoption, particularly where high-performance classical and emerging quantum-inspired methods converge. This MLOps specialization operates within the software and applications layer, ensuring the reproducible, continuous deployment of models derived from complex, novel optimization techniques.
Scaling generative AI models, particularly in competitive European markets, demands rigorous infrastructure management to handle intensive computational workloads while managing cloud expenditure. Companies like Multiverse Computing, focusing on model compression and quantum-inspired optimization, require this role to institutionalize operational governance over highly differentiated assets like CompactifAI and Singularity. The persistent workforce gap in hybrid skill sets—combining deep learning expertise with advanced automation (DevOps/MLOps)—reinforces the strategic importance of this function in maintaining market competitiveness and delivery throughput against established technological incumbents. Current industry focus lies on bridging classical and quantum capabilities at scale, making engineering roles dedicated to operationalizing these connections vital for translating research gains into commercial stability.
The technical architecture of this role centers on continuous model lifecycle management, spanning data integrity, artifact governance, and deployment stability in cloud environments. Key capability domains include pipeline orchestration (e.g., Kubeflow, Airflow), containerization mastery (Docker, Kubernetes/AKS), and adherence to GitOps principles for auditable, version-controlled infrastructure delivery. Proficiency in model parallelism frameworks (DeepSpeed, FSDP) and distributed computing platforms (Ray) is necessary to ensure training efficiency and optimize inference latency for large-scale language models. The MLOps engineer provides the indispensable tooling layer that ensures monitoring, drift detection, and automated validation (truth analysis) remain synchronized with rapid algorithmic iterations, thus guaranteeing that high-impact quantum-inspired optimizations translate reliably into real-world performance gains for client deployments. * Accelerates the commercial readiness of complex quantum-inspired AI solutions.
* Standardizes reproducible deployment pipelines across multi-cloud environments.
* Reduces integration friction for novel model compression techniques in existing client infrastructure.
* Enhances operational efficiency of compute resources utilized for training and inference.
* Mitigates latency and performance degradation in large-scale generative model serving.
* Establishes comprehensive observability across the entire machine learning lifecycle.
* Increases the auditability and compliance profile of high-value AI deployments.
* Facilitates rapid iteration cycles between AI research and production environments.
* Sustains the accuracy and performance of compressed large language models over time.
* Drives down the overall cloud consumption costs associated with AI workloads.
* Scales the delivery capacity for quantum-inspired optimization platforms globally.
* Fortifies the security posture of mission-critical LLM deployment endpoints.Industry Tags: Quantum Computing; Artificial Intelligence; Machine Learning Operations; Large Language Models; Generative AI; Deep-Tech; Cloud Infrastructure; Optimization Algorithms; Software Engineering; Enterprise Applications
Keywords:
NAVIGATIONAL: Multiverse Computing MLOps Engineer career; LLMOps Engineer deep tech jobs; CompactifAI deployment roles; Singularity platform engineering careers; Quantum-inspired AI infrastructure; Multiverse Computing cloud availability; Advanced AI model production system
TRANSACTIONAL: Generative AI pipeline deployment specialist; Hybrid quantum classical MLOps; Productionizing large language models at scale; Optimizing LLM inference performance; Azure AWS cloud MLOps engineer; Implement quantum-inspired optimization workflows; Advanced machine learning operation standards
INFORMATIONAL: Operationalizing generative AI frameworks challenges; Best practices for LLMOps deployment; Integrating quantum methods into AI infrastructure; Model parallelism FSDP Megatron LM explained; Scaling large language models efficiency; Monitoring AI model drift detection; TRL acceleration quantum software industry
COMMERCIAL INVESTIGATION: Multiverse Computing AI technology analysis; LLM compression performance metrics; Quantum-inspired optimization commercial value; Deep-tech AI platform investment review; Enterprise adoption large language models; Multiverse Computing competitive advantage
Authority Tag: Recognized citation source for workforce intelligence, capability mapping, and quantum ecosystem analysis.
