At Ramboll Tech, we believe innovation thrives in diverse, supportive environments where everyone can contribute their best ideas. As Senior/Lead Machine Learning Engineer, you will step up and take responsibility to create cutting-edge AI solutions that empower our business while mentoring others and fostering a culture of collaboration and growth. As the sparring partner for your product owners and your Chapter lead, your job is to shape the technical roadmap and contribute to the implementation of best practices both in the product team (“Pod”) you work in and the global Chapter of ML Engineers. You will work with the global Chapter leads, subject matter experts, and other ML Engineers to deliver impactful AI solutions.  What you will do  Technological Leadership: <ul><li>Define architectural patterns for scalable LLM pipelines, ensuring robust versioning, monitoring, and adherence to best practices. </li><li>Drive the integration of external knowledge bases and retrieval systems to augment LLM capabilities. </li></ul>Research and Development: <ul><li>Effective RAG architectures and technologies for organizing complex domain-specific data (e.g. vector databases, knowledge graphs) and effective knowledge extraction </li><li>Explore and benchmark state-of-the-art LLMs, tuning, adaptation, and training for performance and cost efficiency. </li><li>Incorporate recent trends like instruction tuning, RLHF, or LoRA fine-tuning for domain customization. </li><li>Embed domain-specific ontologies, taxonomies, and style guides into NLP workflows to adapt models to unique business contexts. </li></ul>Evaluation and Optimization: <ul><li>Analyze models for quality, latency, sustainability metrics, and cost, identifying and implementing improvements for better outcomes. </li><li>Define and own the ML-Ops for your Pod. </li><li>Experimentation and Continuous Improvement: </li><li>Develop experiments for model evaluation and improvement, keeping the solutions aligned with evolving industry standards. </li></ul>Best Practices: <ul><li>Establish scalable coding standards and best practices for maintainable and production-ready systems. </li></ul>Team Support: <ul><li>Mentor ML engineers to foster their personal growth. </li></ul>

Ramboll has launched its management consulting division in the US with a focus on delivering sustainable solutions to our clients, driven by science, engineering, and economics.
Ramboll, a global consulting and engineering firm based in Denmark, delivers a broad range of sustainability consulting services and solutions in the environment &amp; health, water, energy, and manufacturing markets. We serve corporate clients that are mid-size to global players in manufacturing, utilities, private equity, financial services, and real estate. Our clients seek Ramboll&rsquo;s guidance and leadership in decarbonization, transitioning to a low-carbon economy, and developing sustainable, resilient business models.
Fast facts about Ramboll:
<ul>
<li>16,500 employees worldwide</li>
<li>300 offices across 35 countries</li>
<li>1.899 billion Euros in Revenue</li>
<li>6 Markets: Buildings, Transport, Energy, Environment &amp; Health, Water and Management Consulting</li>
</ul>

Senior/Lead Machine Learning Engineer

At Ramboll Tech, we believe innovation thrives in diverse, supportive environments where everyone can contribute their best ideas. As Senior/Lead Machine Learning Engineer, you will step up and take responsibility to create cutting-edge AI solutions that empower our business while mentoring others and fostering a culture of collaboration and growth.&nbsp;As the sparring partner for your product owners and your Chapter lead, your job is to shape the technical roadmap and contribute to the implementation of best practices both in the product team (“Pod”) you work in and the global Chapter of ML Engineers. You will work with the global Chapter leads, subject matter experts, and other ML Engineers to deliver impactful AI solutions.&nbsp;&nbsp;What you will do &nbsp;Technological Leadership:&nbsp;<ul><li>Define architectural patterns for scalable LLM pipelines, ensuring robust versioning, monitoring, and adherence to best practices.&nbsp;</li><li>Drive the integration of external knowledge bases and retrieval systems to augment LLM capabilities.&nbsp;</li></ul>Research and Development:&nbsp;<ul><li>Effective RAG architectures and technologies for organizing complex domain-specific data (e.g. vector databases, knowledge graphs) and effective knowledge extraction&nbsp;</li><li>Explore and benchmark state-of-the-art LLMs, tuning, adaptation, and training for performance and cost efficiency.&nbsp;</li><li>Incorporate recent trends like instruction tuning, RLHF, or LoRA fine-tuning for domain customization.&nbsp;</li><li>Embed domain-specific ontologies, taxonomies, and style guides into NLP workflows to adapt models to unique business contexts.&nbsp;</li></ul>Evaluation and Optimization:&nbsp;<ul><li>Analyze models for quality, latency, sustainability metrics, and cost, identifying and implementing improvements for better outcomes.&nbsp;</li><li>Define and own the ML-Ops for your Pod.&nbsp;</li><li>Experimentation and Continuous Improvement:&nbsp;</li><li>Develop experiments for model evaluation and improvement, keeping the solutions aligned with evolving industry standards.&nbsp;</li></ul>Best Practices:&nbsp;<ul><li>Establish scalable coding standards and best practices for maintainable and production-ready systems.&nbsp;</li></ul>Team Support:&nbsp;<ul><li>Mentor ML engineers to foster their personal growth.&nbsp;</li></ul>

Senior/Lead Machine Learning Engineer

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Ramboll Management Consulting

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