As a Senior MLOps 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 Scientist, AI Researchers, Product teams and others in the company have the high-performance infrastructure, automated pipelines, and deployment strategies needed to ship state-of-the-art models and agents at scale.
Who We’re Looking For
- 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 & Platform Engineering
- 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 & Observability
- Automated Pipelines: Design CI/CD and 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.
- Observability and Reliability: Model drift monitoring, resource utilisation, LLM and agent tracing.
Performance & Optimization
- Inference Optimization: Implement techniques to reduce latency and increase throughput (quantisation, distillation, etc…)
- Cold Start Mitigation: Solve scaling bottlenecks for serverless or containerized model deployments.
- Cost Management: Optimize GPU utilization and cloud spend without compromising performance.
AI Enablement
- Support AI Agent Deployment: Define and create tooling and service templates around agent deployment (tool libraries, tracing, default agent frameworks, skills, etc…).
- Enablement for non-technical agent users: Help create workflows and guidance on no-code/low-code agent platforms (n8n, LangSmith, or similar). Create tooling and policies to enable safe usage of local agents such as Claude code.
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.
Links to more information on Prolific
Benefits
External Handbook
Website
Youtube
Privacy Statement
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.
