Senior MLOps Engineer

Mesh-AI Limited; Remote; GBP 70,000 - GBP 90,000; Full time

Job summary

A leading AI technology company in the United Kingdom is seeking a Senior ML/LLMOps Engineer to oversee the AI production lifecycle. You will ensure optimal infrastructure and automated pipelines for data scientists and researchers. Ideal candidates will have over 5 years of experience in cloud infrastructure management and the ML lifecycle. Join us to engage in impactful AI innovation, enjoy a competitive salary, and have the flexibility of remote working within a mission-driven culture.

Benefits

Competitive salary

Remote working opportunities

Impactful, mission-driven culture

Qualifications

• 5+ years experience with cloud infrastructure and infrastructure as code.
• Experience with the ML and LLM lifecycle, including training and optimization.
• Ability to work closely with researchers and data scientists.

Responsibilities

• Design and maintain scalable cloud environments using IaC.
• Manage GPU/TPU resource allocation for training and fine-tuning.
• Build internal services and tools to enhance developer experience.
• Design CI/CD and Continuous Training pipelines.
• Develop reusable patterns for model serving in Kubernetes.
• Optimize vector databases for RAG-based systems.

Skills

Cloud infrastructure management

Infrastructure as Code (IaC)

ML/LLM lifecycle experience

Collaboration with researchers and data scientists

Tools

Terraform

GitHub Actions

MLFlow

Vertex AI Pipelines

Kubernetes

Job description

Overview

Prolific is not just another player in the AI space – we are the architects of the human data infrastructure that's reshaping the landscape of AI development. In a world where foundational AI technologies are increasingly commoditized, it's the quality and diversity of human-generated data that truly differentiates products and models.

The Role

The future of AI development relies on a critical, indispensable component: high-quality human data. Prolific provides the world's largest and most trusted source of this data to the teams pushing the boundaries of AI technology. As a Senior ML/LLMOps Engineer, you will be the backbone of our AI production lifecycle. You will bridge the gap between research and real-world application, ensuring our Data Scientists and AI Researchers have the high-performance infrastructure, automated pipelines, and deployment strategies needed to ship state-of-the-art models at scale. We deploy models and infrastructure responsible for a host of AI tasks, ranging from fraud detection to RAG based search.

Who We’re Looking For

Qualifications

• 5+ years experience with cloud infrastructure and infrastructure as code.
• Previous experience with the ML and LLM lifecycle - training, hosting, optimisation, observability.
• Used to working closely with researchers and data scientists - taking experiments from worksheets into production.
• Strong grasp of ML fundamentals and modern GenAI stack.

What You’ll be Doing

• Infrastructure as Code (IaC): Design and maintain scalable cloud environments (GCP/AWS) using Terraform.
• Resource Provisioning: Manage GPU/TPU resource allocation for training, fine-tuning, and interactive notebooks.
• Custom Tooling: Build internal services and CLI tools to streamline the developer experience for the AI team.

ML & LLM Orchestration

• Automated Pipelines: Design CI/CD/CT (Continuous Training) pipelines using tools such as GitHub Actions, MLFlow, Vertex AI Pipelines. Ensure high quality training data (e.g. introducing a feature store).
• Deployment Methodology: Develop reusable patterns for model serving. Managing service deployments to Kubernetes.
• Vector Infrastructure: Manage and optimize vector databases and embedding pipelines for RAG-based systems.

Performance & Optimization

• Inference Optimization: Implement techniques to reduce latency and increase throughput.
• Cold Start Mitigation: Solve scaling bottlenecks for serverless or containerized model deployments.
• Cost Management: Optimize GPU utilization and cloud spend without compromising performance.

Observability & Reliability

• Traditional MLOps: Monitor for model drift, data skew, and resource utilization.
• LLM-Specific Ops: Implement LLM Tracing to monitor prompts, agent actions and general service health.

Why Prolific is a great place to work

We\'ve built a unique platform that connects researchers and companies with a global pool of participants, enabling the collection of high-quality, ethically sourced human behavioral data and feedback. This data is the cornerstone of developing more accurate, nuanced, and aligned AI systems.

We believe that the next leap in AI capabilities won\'t come solely from scaling existing models but from integrating diverse human perspectives and behaviors into AI development. By providing this crucial human data infrastructure, Prolific is positioning itself at the forefront of the next wave of AI innovation—one that reflects the breadth and the best of humanity.

Working for us will place you at the forefront of AI innovation, providing access to our unique human data platform and opportunities for groundbreaking research. Join us to enjoy a competitive salary, benefits, and remote working within our impactful, mission-driven culture.

Application and privacy

By submitting your application, you agree that Prolific may collect your personal data for recruiting and global organization planning. Prolific\'s Candidate Privacy Notice explains what personal information Prolific may process, where Prolific may process your personal information, its purposes for processing your personal information, and the rights you can exercise over Prolific use of your personal information.