Senior MLOps Engineer

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

• 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

• 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.

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