Machine Learning Engineering Lead
6 days ago
About Ramboll Tech
Ramboll Tech accelerates innovation and digital transformation for the entire Ramboll group and directs all AI-related initiatives within the company. This includes collaborating with markets at Ramboll on their AI projects, as well as working on larger change processes within the corporation and developing proprietary AI products for Ramboll and our clients.
The Job Description
We are seeking a highly skilled and experienced Machine Learning Engineering Lead to lead our machine learning engineering team. The ideal candidate will have extensive experience in designing and implementing large-scale machine learning systems and a strong technical background in machine learning, computer vision, and natural language processing.
Key Responsibilities
- Technical Leadership: Define architectural patterns for scalable machine learning pipelines, ensuring robust versioning, monitoring, and adherence to best practices.
- Research and Development: Effective RAG architectures and technologies for organizing complex domain-specific data (e.g. vector databases, knowledge graphs) and effective knowledge extraction.
- Evaluation and Optimization: Analyze models for quality, latency, sustainability metrics, and cost, identifying and implementing improvements for better outcomes.
- Experimentation and Continuous Improvement: Develop experiments for model evaluation and improvement, keeping the solutions aligned with evolving industry standards.
Requirements
- Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- Experience: Minimum 5 years of experience implementing machine learning projects.
- Leadership Skills: Proven leadership in driving technical projects to successful completion in agile environments.
- Machine Learning Expertise: Strong expertise in machine learning, computer vision, and natural language processing.
What You Will Do
You will define architectural patterns for scalable machine learning pipelines, ensure robust versioning, monitoring, and adherence to best practices. You will also develop experiments for model evaluation and improvement, keeping the solutions aligned with evolving industry standards. Additionally, you will analyze models for quality, latency, sustainability metrics, and cost, identifying and implementing improvements for better outcomes.
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