High Salary Lead Machine Learning Engineer

Job Description

Job description

- At Ramboll Tech, we believe innovation thrives in diverse, supportive environments where everyone can contribute their best ideas. As a 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:

- Define architectural patterns for scalable LLM pipelines, ensuring robust versioning, monitoring, and adherence to best practices.
- Drive the integration of external knowledge bases and retrieval systems to augment LLM capabilities.

Research and Development:

- Effective RAG architectures and technologies for organizing complexdomain-specificdata (e.g. vector databases, knowledgegraphs) and effective knowledgeextraction
- Explore and benchmark state-of-the-art LLMs, tuning, adaptation, and training for performance and cost efficiency.
- Incorporate recent trends like instruction tuning, RLHF, or LoRA fine-tuning for domain customization.
- Embed domain-specific ontologies, taxonomies, and style guides into NLP workflows to adapt models to unique business contexts.

Evaluation and Optimization:

- Analyze models for quality, latency, sustainability metrics, and cost, identifying and implementing improvements for better outcomes.
- Define and own the ML-Ops for your Pod.
- Experimentation and Continuous Improvement:
- Develop experiments for model evaluation and improvement, keeping the solutions aligned with evolving industry standards.

Best Practices:

- Establish scalable coding standards and best practices for maintainable and production-ready systems.

Team Support:

- Mentor ML engineers to foster their personal growth.

Qualifications

How you will succeed in your role

We're looking for someone who is excited to make an impact and grow with us. While not everyone will have all the qualifications listed, you might be a great fit if you bring some of the following. We're working with every team member individually to grow according to their needs and abilities.

Education:

- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

Experience:

- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- Minimum 5 years of experience implementing machine learning projects.
- At least 2 years in a senior or lead role.
- Demonstrated expertise integrating modern LLMs into production systems.

Leadership skills:

- Proven leadership in driving technical projects to successful completion in agile environments
- Strong communication skills to align technical solutions with business goals.
- Ability to mentor and foster innovation within the team.

Technology Skills:

- LLM and RAG Expertise:
- Strong expertise in building Retrieval-Augmented Generation (RAG) architectures and integrating with vector and graph databases.

Transformer and LLM Architectures:

- In-depth experience with modern Transformer-based LLMs (e.g., GPT-4, Claude, Gemini, Llama, Falcon, Mistral)

Model Performance and Optimization:

- Demonstrated ability to fine-tune and optimize LLMs for quality, latency, sustainability and cost-effective performance.
- Programming and NLP Tooling:
- Advanced Python proficiency and expertise with frameworks like PyTorch, TensorFlow, Hugging Face, or LangChain.
- MLOps and Deployment:
- Experience with containerization tools (e.g. Docker, Kubernetes) and workflow management tools (e.g. Azure ML Studio, MLFlow)

Cloud and AI Infrastructure:

- Hands-on experience with (preferably Azure) Cloud environments for scalable AI deployment, monitoring, and optimization.

Document Intelligence:

- Document Processing and knowledge extraction tools

Databases:

- Experience with relational (SQL), NoSQL databases

Data Platforms:

- Familiarity with platforms like Snowflake or Databricks

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