Full-Time Lead Machine Learning Engineer

Lead Machine Learning Engineer at Ramboll Tech

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: Explore effective RAG architectures and technologies for organizing complex domain-specific data (e.g., vector databases, knowledge graphs) and effective knowledge extraction. 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. Establish scalable coding standards and best practices for maintainable and production-ready systems.

Team Support: Mentor ML engineers to foster their personal growth.

Essential Skills: Strong expertise in building Retrieval-Augmented Generation (RAG) architectures and integrating with vector and graph databases. In-depth experience with modern Transformer-based LLMs (e.g., GPT-4, Claude, Gemini, Llama, Falcon, Mistral). Demonstrated ability to fine-tune and optimize LLMs for quality, latency, sustainability and cost-effective performance. Advanced Python proficiency and expertise with frameworks like PyTorch, TensorFlow, Hugging Face, or LangChain. Experience with containerization tools (e.g., Docker, Kubernetes) and workflow management tools (e.g., Azure ML Studio, MLFlow). Hands-on experience with (preferably Azure) Cloud environments for scalable AI deployment, monitoring, and optimization. Experience with relational (SQL), NoSQL databases. Familiarity with platforms like Snowflake or Databricks.

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.

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