• Help improve existing code base by improving memory and compute efficiency; • Develop new automation pipelines for preparing datasets and running experiments; • Move research into production; • Collaborate with a team of accomplished ML scientists and chemists.

Your mission As an applied AI engineer at Perelyn, you will be involved in both internal and external engagements. Your responsibilities include: - Contributing to a wide range of AI-related projects, which span from LLM use cases to computer vision, full-stack chatbot development, and Machine Learning Operations (MLOps) - Supporting research and product development involving AI components, such as Retrieval-Augmented Generation (RAG) or agentic AI - Engaging in discussions to identify and analyze use cases, both internally and externally, and architecting tailored solutions aligned with business requirements - Closely collaborating with the AI Strategy department to ensure successful implementation of AI-driven projects Your profile Hands-on AI and Machine Learning Experience - Practical mentality with proven experience leveraging various AI/ML models - Ability to implement business-driven Generative AI solutions using frameworks like LangChain, Haystack, or LlamaIndex and software development methodologies - Interest or knowledge in classical Machine Learning/Deep Learning techniques (advantageous but not required) - Experience with LLM/AI Agent evaluation, guardrailing, or trustworthy AI Software Engineering and Deployment - Ability to independently create and deploy AI-driven applications, including backend code - Proficient in Python; familiarity with JavaScript, TypeScript, R, or Java is beneficial - Proficient with containerization and deployment strategies (Docker) - Experience building and consuming REST APIs - Understanding of cloud deployment and cloud-native services Data Engineering - Knowledge of data transformation, cleaning, preparation, and model selection - Preferred: Understanding of how to evaluate large datasets - Preferred: Skills in data structuring and storage modeling Communication and Professionalism - This role requires German language skills at C1 level and fluency in English - Effective communicator capable of articulating technical concepts clearly - Confident in representing the company professionally in client interactions - Team-oriented mindset, open to collaboration and constructive feedback Bonus Qualifications - Knowledge of advanced Machine Learning methodologies with libraries like Scikit-learn, PyTorch, TensorFlow - Background in statistics and model evaluation techniques - Familiarity with computer vision technologies - Experience with MLOps frameworks (e.g., MLflow) - Experience integrating LLMs with graph databases (e.g., Neo4j) - Experience with AI-augmented coding technologies (e.g., Claude Code, Gemini CLI) - Basic understanding of frontend technologies (React or Angular advantageous) Why us? Perelyn is an innovative data-driven AI consultancy company headquartered in the heart of Munich. Since 3 years and counting, we are passionate about turning complex data into actionable insights and customer value. Our projects span across different industries and scopes in Germany and beyond, making a real impact on businesses and society at large.    We at Perelyn offer you:  - Competitive salary with performance-based boni and annual review of package - Opportunity to grow and develop your (technical) expertise, with all relevant training & certification costs being covered - A truly flexible working environment with “remote first” policy, opportunities for part-time work and sabbatical, and family-friendly tailormade solutions that fit all - A dynamic, innovative and experienced team of international professionals that genuinely care (exceptional, but humble :)) - Clear and honest communication on KPIs with adequate tools and structures to facilitate your ownership of projects - Strong company culture with modern values based on respect, credibility, collaboration and continuous learning - Bottom-up decision-making, flat hierarchies and modern equipment tailored to your needs - 30 days annual leave per year + a gym-card (Wellpass) subscription, subsidized by the company

• Contribute to a wide range of AI-related projects, spanning LLM use cases, computer vision, full‑stack chatbot development, and Machine Learning Operations (MLOps) • Support research and product development involving AI components, such as Retrieval-Augmented Generation (RAG) and agentic AI • Participate in discussions to identify and evaluate use cases internally and externally, and design tailored solutions aligned with business requirements • Work closely with the AI Strategy department to ensure successful implementation of AI-driven projects

Job Summary We are building CreateOS — a next-generation operating system for modern record labels — and AI is at the center of it. As a Machine Learning Engineer, you will be a core builder on the team responsible for designing, training, deploying, and maintaining the ML systems that power intelligent automation, forecasting, anomaly detection, and agentic workflows across the platform. This is a hands-on engineering role for someone who is equally comfortable prototyping a new model and shipping it to production. You will work directly with the VP of AI & ML Engineering and partner closely with Data Engineering, Analytics, and Product to move AI from experimentation into reliable, scalable production systems embedded in real user workflows. Your immediate impact will be on three of CMG's highest-priority AI initiatives: M&A catalog valuation forecasting, AI-driven A&R discovery, and marketing automation agents — each directly tied to revenue growth and competitive differentiation. Responsibilities Applied Machine Learning - Build and maintain ML models for forecasting, anomaly detection, classification, ranking, and optimization across music industry use cases (catalog valuation, royalty reasoning, A&R intelligence, marketing performance, etc.) - Partner with Analytics & BI to identify, engineer, and validate features that drive meaningful predictive power - Own the full ML lifecycle — from problem framing and data exploration through training, evaluation, deployment, and monitoring MLOps & Production Engineering - Deploy and monitor models in production using modern MLOps tooling - Instrument models for performance tracking, drift detection, and continuous improvement - Implement CI/CD, automated testing, model versioning, and observability for all ML systems - Collaborate with Data Engineering to ensure data quality, feature delivery, and pipeline reliability Agentic AI & LLM Systems (Stretch) - Develop and maintain modular AI agents that automate multi-step workflows across CreateOS (contracts, accounting, distribution, metadata) - Build and iterate on RAG pipelines, retrieval architectures, and semantic search systems grounded in structured business data - Implement guardrails, evaluation frameworks, and safe action boundaries for agentic systems Collaboration & Communication - Translate business problems from non-technical stakeholders into well-scoped ML solutions - Document model design decisions, evaluation results, and known limitations clearly - Contribute to a culture of engineering rigor and responsible AI development - Other duties as assigned Qualifications - 4+ years of software engineering experience in a production environment, with exposure to ML or data science work (academic, professional, or project-based); OR 2+ years of experience specifically as an ML Engineer or Applied Data Scientist - Strong proficiency in Python and ML frameworks (PyTorch, scikit-learn, XGBoost, or similar) - Hands-on experience building, deploying, and monitoring models in cloud environments — GCP strongly preferred (AWS or Azure acceptable); familiarity with services such as Vertex AI, BigQuery, Cloud Functions, and Cloud Run is a strong plus - Solid understanding of modern ML techniques — supervised/unsupervised learning, time series forecasting, embeddings, ranking — and their mathematical foundations - Experience with LLMs and prompt engineering, including building RAG systems or LLM-powered features - Comfortable working with structured and unstructured data at scale - Strong communication skills with the ability to explain complex model behavior to non-technical audiences Preferred Qualifications - MS in Machine Learning, Data Science, Business Analytics, Computer Science, Statistics, or a related quantitative field; OR 4+ years of experience as an ML Engineer or Applied Data Scientist in lieu of an advanced degree - Experience at a startup or high-growth technology company where you owned features end-to-end - Familiarity with agentic frameworks (LangChain, LangGraph, AutoGen, or similar) - Background in music, media, entertainment, or rights/royalties data - Experience with ontology design, knowledge graphs, or semantic data modeling - Contributions to open-source ML projects or published research - Familiarity with AI evaluation practices — hallucination detection, bias auditing, model explainability Pay Scale - $160,000 - $200,000/USD per year - The final compensation within this range will be determined based on the candidate’s experience, skills, and overall fit for the role. Fair Chance Policy In accordance with the Los Angeles County Fair Chance Ordinance, we will consider employment for qualified applicants with criminal histories. We evaluate candidates based on their qualifications and the nature of the offense in relation to the job for which they are applying.