Role Description As a full-stack developer at sandan AI, you will: - Develop and maintain our full-stack applications within the modern JavaScript/TypeScript ecosystem - Design and implement scalable backend services using Node.js and Nest.js - Build high-performance frontend solutions with React.js and Next.js - Design and optimize relational databases (PostgreSQL, MySQL, MS SQL) - Containerize and orchestrate services using Docker and Kubernetes - Develop and integrate REST APIs - Build and operate data pipelines, feature stores, and ML workflows for our agentic systems - Develop, train, and evaluate machine learning models for marketing use cases (attribution, targeting, forecasting, anomaly detection) - Implement Retrieval-Augmented Generation (RAG) pipelines (embeddings, vector stores, re-ranking) - Set up and maintain LLM evaluation frameworks, prompt optimization workflows, and systematic quality benchmarks - Apply code harnesses (especially Claude Code and Codex) throughout the daily development process — from planning and implementation to code review - Collaborate closely with the founding team on the product architecture of agentic systems Qualifications - Several years of professional experience in full-stack development - Strong knowledge of TypeScript and JavaScript - Confident working with React.js, Next.js, Node.js, and Nest.js - Experience with relational databases (PostgreSQL, MySQL, MS SQL) - Hands-on experience with container technologies (Docker, Kubernetes) - Familiarity with REST API design and Git workflows - Solid Python skills, including common data/ML stacks (pandas, NumPy, scikit-learn, PyTorch, or TensorFlow) - Experience across the end-to-end lifecycle of ML models: data preparation, feature engineering, training, validation, deployment, and monitoring - Strong understanding of statistical fundamentals (hypothesis testing, regression, classification, time series) - Experience with modern LLM workflows: prompt engineering, RAG, embeddings, vector databases (e.g. Pinecone, Weaviate, pgvector) - Hands-on experience in model evaluation: metrics, benchmarks, A/B testing, and systematic evaluation frameworks - Experience with notebook-based exploration environments (Jupyter, Colab) and transitioning them into production-ready pipelines - Familiarity with MLOps tools (e.g. MLflow, Weights & Biases, DVC) is a plus - Experience with Claude Code and/or Codex is highly appreciated — otherwise, a strong willingness to actively learn and adopt these tools - Excellent German (C2) and English skills (minimum C1) - Genuine interest in generative AI and agentic systems - Builder mentality: you deliver production-grade software, not just prototypes - Analytical mindset: you make data-driven decisions and critically evaluate model behavior Benefits - A defining role in an AI-native startup with a strong enterprise focus - Competitive salary and employee stock ownership plan (ESOP) - Fully funded access to all relevant code harnesses, LLM APIs, and developer tools (Claude Max, Codex, GitHub Copilot, etc.) - Compute budget for experiments, model training, and ML workloads - Modern hardware setup (MacBook Pro of your choice) - Flexible working hours and remote work options - Office in Berlin and location in Dubai (DIFC AI Campus) - A steep learning curve in Agentic AI, LLM orchestration, and AI-native engineering

• Develop AI-driven financial models for risk management, liquidity prediction, and market analysis. • Work with symbolic AI, logical reasoning, and agentic AI to improve DeFi decision-making. • Collaborate with engineers and researchers on cutting-edge AI tools for finance. • Analyze and process large financial datasets, ensuring AI model accuracy and efficiency. • Adapt traditional Basel II/III risk management frameworks to DeFi environments. • Drive adoption of AI tools for financial analysis and automation.

• Own and execute the global sourcing process, systems, and analytics strategy across GE Vernova • Define and drive the Source‑to‑Pay future‑state operating model , ensuring simplification, standardization, scalability, and strong governance • Lead enterprise sourcing transformation programs from concept through execution, with measurable business outcomes • Conduct sourcing maturity assessments across Business Units and lead targeted improvement roadmaps • Own and evolve the global sourcing systems landscape, including platforms such as SupplierConnect, Sourcegate+, Grid Fusion VMR, Sourcing360, and ERP integrations • Lead sourcing data ingestion into the enterprise data lake; define common metrics, dashboards, and insights • Build scalable analytics capabilities to improve decision‑making, savings identification, compliance, and risk visibility • Define and execute the digital and GenAI roadmap for sourcing , from use‑case identification through deployment and adoption • Partner with IT, finance, legal, and external vendors to modernize sourcing tools and workflows • Drive user adoption, change management, and capability building to fully capture technology value • Benchmark external best practices and emerging technologies to accelerate sourcing maturity • Lead high‑visibility sourcing initiatives, including: GenAI applications, Risk management programs, Commodity and category playbooks, Should‑costing and productivity initiatives, Process excellence and Lean agendas • Own CPO‑level governance , including Sourcing Councils, leadership operating rhythms, and enterprise reviews • Prepare executive‑ready materials for ELT, Board, and senior leadership forums • Lead, coach, and develop a global team responsible for sourcing transformation, systems, and analytics • Drive enterprise capability‑building programs (digital, GenAI, sourcing foundations, analytics fluency) • Embed Lean, Kaizen, and continuous improvement principles across sourcing teams • Foster a culture of accountability, data‑driven decision making, and innovation

• Design, build, and ship production-grade data and ML systems • Apply analytical, statistical, and machine learning techniques to collect, analyze, and interpret large cybersecurity data sets • Develop, test, and maintain backend services, APIs, and data pipelines • Collaborate closely with software engineering, product, detection engineering, and security engineering teams • Own code quality across the stack • Operationalize models with appropriate monitoring, versioning, retraining, and rollback strategies (MLOps) • Contribute to product, services, and detection engineering roadmap by identifying where data science and engineering investment will measurably improve outcomes for analysts and clients • Develop data-driven solutions that ship