Sigma Software Group

• Design, develop, and optimize scalable Machine Learning models for advertising intelligence and forecasting systems • Analyze large-scale historical and real-time datasets to improve forecasting accuracy and monetization strategies • Build and maintain distributed data processing pipelines using Spark and related Big Data technologies • Develop production-ready ML solutions using Python within AWS cloud infrastructure • Research, evaluate, and implement Machine Learning algorithms suitable for high-load AdTech environments • Improve model performance, scalability, reliability, and operational efficiency • Collaborate with Data Engineers, Product Managers, and distributed engineering teams to deliver end-to-end ML solutions • Contribute to ML architecture decisions, experimentation approaches, and engineering best practices • Monitor, validate, and optimize ML model quality and forecasting performance in production environments • Produce technical documentation related to ML pipelines, models, and distributed systems • Mentor engineers and support technical growth within the team • Drive innovation in Machine Learning and AdTech technologies

• Design, develop, and optimize scalable Machine Learning models for advertising intelligence and forecasting systems • Analyze large-scale historical and real-time datasets to improve forecasting accuracy and monetization strategies • Build and maintain distributed data processing pipelines using Spark and related Big Data technologies • Develop production-ready ML solutions using Python within AWS cloud infrastructure • Research, evaluate, and implement Machine Learning algorithms suitable for high-load AdTech environments • Improve model performance, scalability, reliability, and operational efficiency • Collaborate with Data Engineers, Product Managers, and distributed engineering teams to deliver end-to-end ML solutions • Contribute to ML architecture decisions, experimentation approaches, and engineering best practices • Monitor, validate, and optimize ML model quality and forecasting performance in production environments • Produce technical documentation related to ML pipelines, models, and distributed systems • Mentor engineers and support technical growth within the team • Drive innovation in Machine Learning and AdTech technologies

• Design, develop, and maintain backend services using Go • Contribute to the development of a self-hosted SCIM solution • Deploy and support applications running in Kubernetes environments • Participate in architecture and technical design discussions • Collaborate with platform engineering and DevOps teams • Implement integrations with IAM and directory services • Support CI/CD and automation initiatives • Contribute to cloud-native engineering best practices • Troubleshoot and optimize platform performance and reliability

• Develop and integrate ML, NLP, and Generative AI models • Build and optimize agentic workflows using LangChain, LangGraph, or similar frameworks • Develop LLM-powered solutions using modern Generative AI technologies • Prototype internal tools and interfaces with Streamlit or Gradio • Collaborate with engineering and product teams to deliver AI-driven features • Manage development and runtime environments with Docker • Maintain clean and scalable code using Git-based workflows • Query and analyze structured data using SQL • Contribute to model deployment and operations in a cloud environment (primarily Azure)

• Design, develop, and optimize scalable Machine Learning models for advertising intelligence and forecasting systems • Analyze large-scale historical and real-time datasets to improve forecasting accuracy and monetization strategies • Build and maintain distributed data processing pipelines using Spark and related Big Data technologies • Develop production-ready ML solutions using Python within AWS cloud infrastructure • Research, evaluate, and implement Machine Learning algorithms suitable for high-load AdTech environments • Improve model performance, scalability, reliability, and operational efficiency • Collaborate with Data Engineers, Product Managers, and distributed engineering teams to deliver end-to-end ML solutions • Contribute to ML architecture decisions, experimentation approaches, and engineering best practices • Monitor, validate, and optimize ML model quality and forecasting performance in production environments • Produce technical documentation related to ML pipelines, models, and distributed systems • Mentor engineers and support technical growth within the team • Drive innovation in Machine Learning and AdTech technologies

• Design, develop, and maintain robust software applications using TypeScript/JavaScript, Node.js, NestJS, and React.js • Implement new functionality from scratch, including role-based access control services, API integration services, and custom endpoints • Work in an AI-augmented development environment, leveraging tools like Claude Code, Cursor, and Microsoft Copilot • Collaborate closely with stakeholders to define, design, and deliver new features • Ensure code quality, security, and performance through critical evaluation of AI-generated outputs • Participate in SCRUM ceremonies and contribute to team planning, estimation, and retrospectives

• Develop detailed project plans in collaboration with engineering leadership, including resource allocation and budget planning • Coordinate, track, and deliver technical projects spanning multiple teams (engineering, product, threat intelligence, detection, customer success) • Facilitate all Scrum ceremonies and Agile events to maintain team productivity and cohesion • Ensure projects are delivered according to scope, schedule, budget, and quality standards • Plan, manage, and monitor project execution; ensure on-time delivery within allocated budgets • Conduct project risk analysis, proactively track and communicate risks to stakeholders and customers • Create and maintain transparent, easy-to-read project documentation and reports • Establish and continuously improve project management best practices, including tools, templates, and rules of engagement • Drive change and efficiency in collaboration with internal and external partners • Be a resource and people manager: participate in hiring planning with Recruiting, support onboarding, and ensure professional growth of team members • Drive continuous improvements within the Competence Center, contribute to PMO activities and PM training initiatives, and share lessons learned and best practices to enhance project lifecycle efficiency • Contribute to project lifecycle improvement through lessons learned and best‑practice sharing

• Work with stakeholders and participate in solution discovery, initial design, and estimation • Design scalable and maintainable architectures aligned with business goals • Take ownership of key architectural decisions and ensure long-term sustainability • Evaluate alternatives and document the rationale behind key architectural decisions • Guide engineering teams and ensure implementation aligns with architectural vision • Ensure implementation stays aligned with the architecture throughout all project iterations • Design solutions involving integrations, automation, and data flows, including AI components where appropriate • Ensure that AI usage is practical, controlled, and aligned with system constraints • Ensure scalability, performance, and reliability under growing load and complexity • Define measurable targets for quality attributes and track architecture fitness against them • Create reference implementations and support teams in solving complex technical challenges • Balance technical excellence with budget and schedule constraints, avoiding unnecessary over-engineering • Build consensus with engineering teams and communicate architectural decisions constructively • Ensure that solutions deliver measurable business value

• Develop detailed project plans in collaboration with engineering leadership, including resource allocation and budget planning • Coordinate, track, and deliver technical projects spanning multiple teams (engineering, product, threat intelligence, detection, customer success) • Facilitate all Scrum ceremonies and Agile events to maintain team productivity and cohesion • Ensure projects are delivered according to scope, schedule, budget, and quality standards • Plan, manage, and monitor project execution; ensure on-time delivery within allocated budgets • Conduct project risk analysis, proactively track and communicate risks to stakeholders and customers • Create and maintain transparent, easy-to-read project documentation and reports • Establish and continuously improve project management best practices, including tools, templates, and rules of engagement • Drive change and efficiency in collaboration with internal and external partners • Be a resource and people manager: participate in hiring planning with Recruiting, support onboarding, and ensure professional growth of team members • Drive continuous improvements within the Competence Center, contribute to PMO activities and PM training initiatives, and share lessons learned and best practices to enhance project lifecycle efficiency • Contribute to project lifecycle improvement through lessons learned and best-practice sharing

• Own new business development across DACH • Identify, qualify, and pursue opportunities in manufacturing, tech, financial services, and enterprise AI/modernisation programmes • Manage the full sales cycle from first conversation through negotiation and closing • Design and tune niche, value-based offerings for the German market together with practice and delivery teams • Lead pre-sales activity with solution architects and technical experts • Build and manage local partner relationships with AWS, Databricks, and Microsoft • Prepare proposals, commercial offers, and presentations • Manage RFI/RFP and bidding processes • Maintain a disciplined pipeline in CRM and deliver against revenue targets • Represent the company at industry events and client meetings across the region