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- Tasks: Design and maintain scalable ML platforms for AI-driven content creation.
- Company: Join a leading creative agency at the forefront of AI advancements.
- Benefits: Enjoy remote work options, competitive salary, and career growth opportunities.
- Why this job: Be part of an innovative team driving cutting-edge AI and ML infrastructure.
- Qualifications: 3+ years in software engineering or MLOps, with strong skills in Python and cloud platforms.
- Other info: Collaborate with top-tier engineers and data scientists in a fast-paced environment.
The predicted salary is between 43200 - 72000 £ per year.
Location: Remote / Hybrid
Job Type: Full-time
About the Role
Chapter 2 is working with a leading creative agency to develop scalable machine learning platforms for AI-driven content creation. This role is perfect for an MLOps + DevOps Engineer who thrives in fast-paced environments, takes ownership, and has experience building infrastructure for large-scale AI and ML applications. You will be instrumental in developing automated, scalable, and high-performance ML infrastructure to support generative AI workflows and large language models (LLMs) in production.
What You’ll Do
- Design, build, and maintain scalable ML platforms for model development, experimentation, and production workflows.
- Automate ML infrastructure deployment, including data pipelines, model training, validation, and deployment.
- Manage the full ML lifecycle, from model versioning to deployment, monitoring, and retraining.
- Optimise large language model (LLM) operations, ensuring efficient fine-tuning, deployment, and performance monitoring.
- Collaborate closely with data scientists and engineers to develop and deploy ML models at scale.
- Optimise performance for inference and training across GPUs and cloud-based architectures.
- Ensure security and compliance for ML platforms handling sensitive data.
- Evaluate and integrate MLOps tools (MLflow, Kubeflow, etc.) to enhance efficiency.
- Implement monitoring and alerting systems to detect anomalies and maintain model reliability.
What We’re Looking For
- 3+ years of experience in software engineering, infrastructure, or MLOps roles.
- Proven expertise in building and maintaining ML platforms at scale.
- Hands-on experience with cloud platforms (AWS, GCP, or Azure) for ML workloads.
- Strong proficiency with Docker, Kubernetes, and infrastructure automation (Terraform, CloudFormation).
- Solid programming skills in Python and familiarity with ML frameworks like TensorFlow, PyTorch.
- Experience designing CI/CD pipelines for ML workflows and deployment automation.
- Exposure to LLM Ops, including managing fine-tuning and deployment of large language models.
- Strong problem-solving skills and ability to troubleshoot complex ML infrastructure issues.
- Ability to work in a fast-paced, high-growth environment with a product-oriented mindset.
- Bonus: Experience with big data tools (Spark, Kafka) and feature stores.
Why Join Us?
- Work on cutting-edge AI and ML infrastructure supporting generative AI products.
- Be part of a high-impact, innovative team driving AI advancements.
- Competitive salary, benefits, and career growth opportunities.
- Collaborate with top-tier engineers and data scientists in the AI space.
Excited? Let’s talk. Apply now with your resume and portfolio!
Platform Engineer employer: Chapter 2
Contact Detail:
Chapter 2 Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Platform Engineer
✨Tip Number 1
Familiarise yourself with the specific tools and technologies mentioned in the job description, such as Docker, Kubernetes, and cloud platforms like AWS or GCP. Having hands-on experience with these will not only boost your confidence but also demonstrate your readiness for the role.
✨Tip Number 2
Engage with the MLOps community through forums, webinars, or local meetups. Networking with professionals in the field can provide insights into current trends and challenges, which you can discuss during interviews to show your enthusiasm and knowledge.
✨Tip Number 3
Prepare to discuss your previous projects that involved building scalable ML platforms or automating ML workflows. Be ready to explain your thought process, the challenges you faced, and how you overcame them, as this will highlight your problem-solving skills.
✨Tip Number 4
Research the company and its recent projects related to AI and ML. Understanding their work will allow you to tailor your conversation during interviews, showing that you're genuinely interested in contributing to their innovative team.
We think you need these skills to ace Platform Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in MLOps, DevOps, and software engineering. Focus on specific projects where you've built or maintained ML platforms, and include any cloud platform expertise you possess.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Mention how your skills align with their needs, particularly in automating ML infrastructure and optimising large language models.
Showcase Relevant Projects: If you have a portfolio, include examples of past projects that demonstrate your experience with Docker, Kubernetes, and CI/CD pipelines for ML workflows. This will give them a clear picture of your capabilities.
Highlight Problem-Solving Skills: In your application, emphasise your problem-solving abilities and provide examples of how you've tackled complex ML infrastructure issues in previous roles. This is crucial for a fast-paced environment like theirs.
How to prepare for a job interview at Chapter 2
✨Showcase Your Technical Skills
Be prepared to discuss your experience with cloud platforms like AWS, GCP, or Azure. Highlight specific projects where you've built and maintained ML platforms, and be ready to dive into the technical details of your work with Docker, Kubernetes, and infrastructure automation tools.
✨Demonstrate Problem-Solving Abilities
Expect questions that assess your troubleshooting skills in complex ML infrastructure scenarios. Prepare examples from your past experiences where you successfully identified and resolved issues, particularly in high-pressure environments.
✨Familiarise Yourself with MLOps Tools
Research and understand popular MLOps tools such as MLflow and Kubeflow. Be ready to discuss how you've integrated these tools into your workflows to enhance efficiency and manage the ML lifecycle effectively.
✨Emphasise Collaboration Skills
Since this role involves working closely with data scientists and engineers, prepare to talk about your collaborative experiences. Share examples of how you've worked in teams to develop and deploy ML models, focusing on communication and teamwork.
