AI Engineer M F D

Key responsibilities 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 To succeed and thrive in the role, you bring: A degree in Computer Science, Software Engineering, AI, Machine Learning, or similar- or equivalent professional experience 3+ years of professional AI engineering/applied data science experience, including hands-on experience with AI, NLP, machine learning, deep learning, or language-model-based applications Strong Python skills and experience building clean, maintainable, and production-ready software Hands-on experience with GenAI or LLM-based solutions or open-source models Solid understanding of software engineering practices (testing, CI/CD, version control, etc.) Experience with model evaluation, monitoring, or experiment tracking tools (i.e., MLflow or similar). Ability to work in cross-functional, agile teams and communicate clearly in English Nice to have: Experience with cloud platforms such as Google Cloud or similar Familiarity with RAG architectures, embeddings, vector databases, and retrieval techniques Exposure to fine-tuning or model optimization approaches Experience with Kedro for building modular, reproducible data and ML pipelines, and KServe for scalable, production-grade model deployment and inference on Kubernetes Knowledge of agentic workflows, tool-calling systems, agentic search, MCP, or A2A integration patterns What we offer : Employment based on an employment contract, along with a comprehensive benefits package Training and development programs, as well as access to an e-learning platform Onboarding program with the support of a dedicated Buddy Participation in an annual, company-wide integration event A work environment based on Scandinavian organizational culture Opportunities for growth through our internal program Benefits: Sharing the costs of sports activities Private medical care Sharing the costs of foreign language classes Sharing the costs of professional training & courses Life insurance Integration events Corporate gym Corporate sports team Coffee / tea Parking space for employees Extra social benefits Holiday funds Christmas gifts Employee referral program Charity initiatives Bicycle parking Modern and ergonomic office Yoga in the office