AI/ML Engineer

About The Company

Sunday Natural is a renowned leader in the health and wellness industry, committed to delivering high-quality, natural products that promote holistic well-being. Founded in 2013, the company emerged from a deep-rooted passion for health, healing, and personal growth. With a focus on purity, sustainability, and innovation, Sunday Natural offers a diverse range of vegan, eco-friendly products packaged predominantly in recyclable glass. The company operates a state-of-the-art research and development facility in Berlin and collaborates with leading research institutes, scientists, and industry professionals to continuously enhance its offerings. With over 350 employees from more than 45 nationalities, Sunday Natural fosters a collaborative, inclusive, and dynamic work environment dedicated to making a positive impact on individual health and the planet.

About The Role

We are seeking an experienced AI/ML Engineer to join our Central Data Platform team at Sunday Natural. This pivotal role involves taking ownership of our machine learning and Generative AI infrastructure that powers advanced analytics and AI-driven applications across the organization. As an AI/ML Engineer, you will design, develop, and maintain end-to-end machine learning pipelines in Vertex AI, ensuring models transition smoothly from prototype to production while maintaining compliance, scalability, and cost-efficiency. You will be instrumental in pioneering GenAI capabilities, including retrieval augmented generation, embeddings, and AI-powered applications such as chatbots, personalization, and semantic search. Your work will bridge the gap between data engineering, data analysis, and business stakeholders, translating technical solutions into measurable business outcomes. This role offers an exciting opportunity to influence how AI and machine learning are delivered, scaled, and governed at Sunday Natural, fostering innovation while embedding responsible AI practices.

Qualifications

To be successful in this role, you should possess 5 to 7 years of experience in Data Engineering or ML Operations, with at least 3 years dedicated to deploying and managing ML pipelines in production environments. A degree in Computer Science, Data Science, Engineering, or a related field is required; a PhD is considered a plus. You must demonstrate expert proficiency in Python and key ML/DL libraries such as scikit-learn, TensorFlow, PyTorch, Hugging Face, and LangChain. Strong experience with BigQuery, dbt, and SQL for data and feature preparation is essential. Hands-on experience with Vertex AI or equivalent cloud services (AWS/GCP) for pipeline orchestration, deployment, and monitoring is required. Familiarity with ML Ops frameworks like MLflow or TFX, containerization tools such as Docker and Kubernetes, and vector databases like Pinecone, FAISS, or Milvus is also necessary. Proven success in delivering ML or GenAI use cases with measurable business impact, coupled with excellent stakeholder communication skills, will be highly valued. A systems thinker with a strong ethical grounding in responsible AI, balancing innovation with operational reliability and cost management, is ideal.

Responsibilities

Your core responsibilities will include designing, building, and maintaining modular, reusable ML pipelines within Vertex AI Pipelines, covering all stages from training and evaluation to deployment, monitoring, and retraining. You will develop GenAI capabilities, including embeddings, retrieval pipelines, vector databases, and RAG frameworks for applications such as chatbots, personalization, and semantic search. Collaboration with Data Engineers and Analysts to build and manage feature stores and reusable datasets is vital for ensuring data consistency and efficiency. You will productionize workflows using Prefect orchestration and establish CI/CD pipelines in Bitbucket to automate deployment processes. Continuous evaluation, drift detection, and performance monitoring will be part of your routine, along with implementing rollback strategies and retraining triggers to maintain model accuracy. Embedding GDPR compliance, RBAC, anonymization, explainability, fairness, and auditability into all models and pipelines is a key aspect of your role. Documenting feature, model lineage, and inference workflows, as well as partnering with Data Governance teams on ethical AI frameworks, will ensure transparency and accountability. You will translate complex technical capabilities into business-friendly language, clearly communicating trade-offs related to accuracy, latency, and cost to non-technical stakeholders. Additionally, mentoring Data Engineers in ML Ops and GenAI techniques, and contributing to internal AI/ML communities and best practices, will be part of your contributions.

Benefits

• Enjoy a generous 28 days of paid time off annually, plus up to five additional days off between Christmas and New Year.
• We provide an attractive discount on all our products, from essential oils to vitamins and nutrients.
• Our employees benefit from a membership to Urban Sports Club, along with options such as Swapfiets bike rental or a subsidy for the Deutschlandticket.
• We embrace a hybrid working model, allowing one to two days per week working from home, with up to two weeks per year working abroad.
• Our employees also enjoy discounts of up to 50 percent on various brands including Flaconi, Bosch, Apple, and Expedia.
• You will have the opportunity to take ownership of projects, contribute innovative ideas, and implement your expertise in a supportive environment.
• Our inviting office spaces feature organic teas and superfoods, fostering a vibrant and healthy workplace.
• We celebrate achievements and milestones with regular events such as tea & office parties, Christmas and summer feasts, and influencer events like Women's Day, ensuring a lively and inclusive company culture.

Equal Opportunity

Sunday Natural is committed to fostering an inclusive and diverse workplace. We believe that a variety of perspectives and backgrounds enrich our team and drive innovation. We highly encourage women and candidates from underrepresented backgrounds to apply, even if