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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 competitive salary, remote work options, 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 cloud and programming skills.
- Other info: Collaborate with top-tier engineers and data scientists in a fast-paced environment.
The predicted salary is between 43200 - 72000 £ per year.
Job Title: MLOps + DevOps (Platform) Engineer
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\’ll 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
Familiarize yourself with the specific MLOps tools mentioned in the job description, like MLflow and Kubeflow. Having hands-on experience or even personal projects showcasing these tools can set you apart from other candidates.
✨Tip Number 2
Highlight your experience with cloud platforms such as AWS, GCP, or Azure. Be prepared to discuss specific projects where you've deployed ML workloads, as this will demonstrate your practical knowledge and ability to handle real-world challenges.
✨Tip Number 3
Showcase your programming skills in Python and your familiarity with ML frameworks like TensorFlow and PyTorch. Consider preparing a small portfolio of projects that illustrate your coding abilities and understanding of machine learning concepts.
✨Tip Number 4
Emphasize your problem-solving skills and your experience in fast-paced environments. Prepare examples from your past work where you successfully troubleshot complex ML infrastructure issues or optimized performance, as this aligns well with the role's requirements.
We think you need these skills to ace Platform Engineer
Some tips for your application 🫡
Tailor Your Resume: Make sure your resume highlights relevant experience in MLOps, DevOps, and software engineering. Focus on projects where you've built or maintained ML platforms, and emphasize your proficiency with tools like Docker, Kubernetes, and cloud platforms.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI and ML infrastructure. Mention specific projects that demonstrate your ability to automate ML workflows and optimize performance. Show how your skills align with the company's goals.
Showcase Relevant Projects: If you have a portfolio, include examples of your work related to ML platforms, CI/CD pipelines, or large language models. Highlight any innovative solutions you've implemented and the impact they had on previous projects.
Highlight Collaboration Skills: Since the role involves working closely with data scientists and engineers, emphasize your teamwork and communication skills. Provide examples of successful collaborations that led to effective ML model deployments.
How to prepare for a job interview at Chapter 2
✨Showcase Your Experience with ML Platforms
Be prepared to discuss your previous experience in building and maintaining scalable ML platforms. Highlight specific projects where you automated ML infrastructure deployment and managed the full ML lifecycle.
✨Demonstrate Cloud Proficiency
Since cloud platforms are crucial for this role, make sure to share your hands-on experience with AWS, GCP, or Azure. Discuss how you've utilized these platforms for ML workloads and any challenges you overcame.
✨Highlight Your Collaboration Skills
This position requires close collaboration with data scientists and engineers. Be ready to provide examples of how you've worked effectively in teams to develop and deploy ML models at scale.
✨Prepare for Technical Questions
Expect technical questions related to Docker, Kubernetes, and CI/CD pipelines for ML workflows. Brush up on your knowledge of these tools and be ready to explain how you've used them in past projects.
