AI Engineer

• Lead deployment of AI in content creation
• Define principles and workflows for AI content
• Design scalable content workflows
• Provide senior editorial leadership

Senior Content Lead – AI Enabled Market Impact

• Independently deliver scoped AI, GenAI, NLP, and RAG features of moderate complexity
• Develop and enhance RAG pipelines, including document parsing and ingestion, chunking and metadata strategies, query transformation, retrieval and ranking, response generation and grounding
• Build GenAI features using LLM APIs, structured prompting, and orchestration frameworks (e.g., LangChain, LangGraph, DSPy, etc.)
• Evaluate AI system performance using practical methods such as retrieval metrics, response quality assessment, hallucination analysis, latency measurement, cost analysis, and failure-case testing
• Translate requirements into user stories and provide implementation plans

• Build AI-powered features at scale using the latest advancements in LLMs
• Research, prototype, and build advanced NLP/ML models to enable automated resolution of end-user problems
• Design and conduct rigorous experiments and evaluations to enhance model accuracy and reliability
• Refine prompts and tune hyper-parameters to maximize the performance of cutting-edge retrieval and generative AI systems
• Work closely with researchers, engineers, and product teams to define feature scope and implementation strategies
• Mentor junior team members and engage in collaborative learning with peers to promote knowledge sharing
• Contribute to technical reviews and share expertise in ML methodologies and best practices
• Writing clean, maintainable, and well-structured code to meet delivery goals

Principal AI Engineer

• Independently deliver scoped AI, GenAI, NLP, and RAG features of moderate complexity
• Develop and enhance RAG pipelines, including document parsing and ingestion, chunking and metadata strategies, query transformation, retrieval and ranking, response generation and grounding
• Build GenAI features using LLM APIs, structured prompting, and orchestration frameworks (e.g., LangChain, LangGraph, DSPy, etc.)
• Evaluate AI system performance using practical methods such as retrieval metrics, response quality assessment, hallucination analysis, latency measurement, cost analysis, and failure-case testing
• Understand engineering trade-offs across model quality, latency, cost, reliability, maintainability, and implementation complexity
• Own features end-to-end – from clarification and experimentation to deployment and initial support
• Translate requirements into user stories and provide implementation plans, as well as own features end-to-end throughout the software development lifecycle - from clarification and experimentation to deployment and initial support
• Identify risks, dependencies, and data limitations early and propose workable solutions
• Challenge unclear requirements and contribute with pragmatic, value-driven alternatives
• Stay close to new developments in LLMs, RAG, prompt optimization, model evaluation, fine-tuning, and applied AI frameworks, and assess where they create real business impact

Mid-Senior AI Engineer

• Design, develop, and ship AI-driven capabilities—including agents, intelligent assistants, context-aware prediction, and generative AI—using Python, top-tier LLMs, TypeScript, React, Canvas, and WebGL
• Engineer high-performing AI workflows leveraging LLMs (OpenAI, Anthropic Claude, Google Gemini), prompt engineering, and generative techniques—ensuring reliability, scalability, and maintainability
• Execute end-to-end AI projects from conceptualization and prototyping to production code, collaborating closely with product managers, designers, and engineers
• Experiment and iterate on AI capabilities within your team domain, staying ahead of emerging AI tools, frameworks, and techniques
• Champion best practices in AI system development—including evaluations, LLMOps, CI/CD, observability, and ethical AI principles
• Measure and optimize AI performance, establishing benchmarks for user engagement, model accuracy, latency, error rate, and drift
• Write clean, well-tested, maintainable code, participating in code reviews and technical discussions across the stream
• Troubleshoot and resolve production issues, ensuring high availability and performance

LLM Engineer

• This is a chance to own the product of a full-stack AI infrastructure platform from a near-blank sheet.
• The technology is real and already deployable, and the opportunity now is to shape it into a product: defining what Granite sells, how it is packaged and priced, and what gets built next.
• You will own the full product and technology scope, keep the build-and-sell motion aligned as the company deploys and grows, and eventually build the team to run it.
• It is a C-level mandate with direct founding-team influence, reporting to the CEO, with the room to define offerings, pricing, and roadmap rather than run a mature function inside an established hierarchy.
• Own the full product scope: define what Granite sells, how it is packaged and priced, and what gets built next
• Take technical ownership of the platform across compute, data layer, agent runtime, and model serving, and form a clear view on product gaps and deployment priorities
• Map offerings and pricing to customer segments and ship the first defined, sellable product
• Build the compliance layer into the regulated offering and sequence the roadmap against real demand
• Keep the build-and-sell motion aligned as the company deploys and grows
• Make the company's first product and engineering hires, and build the function to run the roadmap at scale
• Represent the product externally to technical and enterprise buyers

Machine Learning Engineer jobs in Denmark

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Senior Content Lead – AI Enabled Market Impact

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Mid-Senior AI Engineer

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Chief Product Officer – AI Infrastructure