• Build and evolve the infrastructure foundations that support Fanvue’s move toward a service-oriented architecture • Enable stream teams to deploy and operate services independently using platform-provided tooling and patterns • Design and maintain AWS infrastructure using AWS CDK (TypeScript), with a strong focus on safety, reuse, and automation • Own and improve CI/CD pipelines, ensuring fast, reliable, and repeatable deployments • Support database architecture evolution, including Aurora PostgreSQL ServerlessV2, service-specific data stores, and scaling strategies • Build internal platform tools and automation to reduce operational toil and improve developer experience • Improve observability and reliability through CloudWatch dashboards, alarms, and SLIs/SLOs • Participate in on-call rotation, lead incident response when needed, and contribute to high-quality post-mortems • Act as a consultative partner to stream teams, helping them make good infrastructure decisions without slowing them down • Contribute to documentation, runbooks, and knowledge sharing to eliminate single points of failure

• Build and extend backend services that power NegotiateAI’s AI-driven procurement platform • Implement LLM- and RAG-based pipelines for spend categorization, benchmarking, and supplier analysis • Develop and maintain agentic workflows that automate analysis, recommendations, and follow-ups • Contribute to the Buyer Portal frontend (React), building clean, usable interfaces for procurement teams • Use AI-assisted coding tools (e.g., Cursor) to accelerate development, debugging, and iteration • Integrate with customer data sources (ERPs, invoices, supplier files) and handle messy, real-world datasets • Collaborate closely with the founding engineer on architecture, trade-offs, and execution • Write clear, maintainable code with strong attention to performance, reliability, and observability

• Architect and guide the design of a scalable, secure ML platform supporting the full ML lifecycle, from data ingestion to model monitoring. • Design and implement infrastructure for model training, hyperparameter tuning, experiment tracking, and model registry. • Orchestrate ML workflows using tools such as Kubeflow, SageMaker, MLflow, or similar. • Collaborate with Data Scientists, MLOps engineers, Data Engineers, and Product Engineering to define best practices for reproducibility, governance, and CI/CD for ML. • Partner with Data Engineers to build robust data pipelines for model-ready datasets. • Optimize ML workload performance across compute and storage layers using cloud-native and open-source solutions. • Lead technical discussions, mentor junior engineers, and help set the technical vision for the ML platform roadmap. • Ensure compliance with security, privacy, and regulatory requirements throughout the ML lifecycle.

• Tworzeniu i utrzymywaniu rozwiązań w oparciu o Microsoft Power Platform (Power Apps, Power Automate, Power BI etc.) • Projektowaniu i wdrażaniu rozwiązań wspieranych przez AI, w tym: Microsoft Copilot Studio (tworzenie niestandardowych Copilotów i integracji z aplikacjami biznesowymi) • AI Builder (automatyzacja procesów i analityka danych z wykorzystaniem modeli AI) • Integracja z usługami Azure AI / Azure OpenAI Service • Pobieraniu i integrowaniu danych z różnych źródeł (SharePoint, SQL, Dataverse, API, Dynamics 365) • Współpracy z zespołem projektowym i wspieraniu innych działów w budowaniu rozwiązań Power Platform • Dzieleniu się wiedzą i promowaniu wykorzystania technologii Power Platform oraz Copilot