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- Tasks: Design and maintain scalable cloud environments for AI production lifecycle.
- Company: Prolific, a leader in human data infrastructure for AI development.
- Benefits: Competitive salary, remote work, and a mission-driven culture.
- Why this job: Join us at the forefront of AI innovation and make a real impact.
- Qualifications: 5+ years in cloud infrastructure and ML lifecycle experience.
- Other info: Dynamic environment with opportunities for groundbreaking research.
The predicted salary is between 48000 - 72000 £ per year.
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 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 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.
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.
Senior MLOps Engineer employer: Prolific - UK Job Board?
Contact Detail:
Prolific - UK Job Board? Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior MLOps Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to MLOps and AI. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common MLOps questions and scenarios. Practice explaining your past experiences and how they relate to the role at Prolific. Confidence is key!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining the Prolific team.
We think you need these skills to ace Senior MLOps Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Senior MLOps Engineer role. Highlight your cloud infrastructure experience and any relevant projects you've worked on, especially those involving ML and LLM lifecycles.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about AI and how your background makes you a perfect fit for Prolific. Share specific examples of how you've bridged the gap between research and production in your previous roles.
Showcase Your Technical Skills: Don’t forget to mention your expertise in Infrastructure as Code, CI/CD pipelines, and any tools like Terraform or GitHub Actions. We want to see how you can contribute to our high-performance infrastructure!
Apply Through Our Website: For the best chance of getting noticed, make sure to apply directly through our website. This way, we can easily track your application and get back to you faster!
How to prepare for a job interview at Prolific - UK Job Board?
✨Know Your Tech Stack
Make sure you’re well-versed in the cloud infrastructure and tools mentioned in the job description, like GCP, AWS, Terraform, and CI/CD pipelines. Brush up on your knowledge of ML fundamentals and the GenAI stack to show you’re ready to hit the ground running.
✨Showcase Your Collaboration Skills
Since the role involves working closely with researchers and data scientists, prepare examples of past experiences where you successfully bridged the gap between research and production. Highlight how you’ve taken experiments from worksheets into real-world applications.
✨Prepare for Technical Questions
Expect to dive deep into the ML and LLM lifecycle during your interview. Be ready to discuss your experience with training, hosting, optimisation, and observability. Practise explaining complex concepts in a simple way, as this will demonstrate your understanding and communication skills.
✨Demonstrate Problem-Solving Abilities
Think of specific challenges you’ve faced in previous roles, especially around performance optimisation and cost management. Be prepared to discuss how you tackled issues like cold start mitigation or resource allocation, showcasing your analytical thinking and innovative solutions.
