• Own the reliability of the event-driven messaging layer, including backpressure management, idempotency, dead-letter handling, and retry strategies. • Build and operate the infrastructure that runs LLM orchestration workloads at scale. • Own the operational data layer for the CI runtime, including state management, session persistence, and real-time data access patterns. • Own observability for the CI platform, including structured logging, distributed tracing (OpenTelemetry), and error tracking (Sentry). • Maintain and harden the interfaces between CI and downstream platforms, including contract testing, versioning, and failure handling. • Conduct code reviews and mentor team members on Python engineering practices and production readiness. • Own production support for CI infrastructure, including on-call responsibilities and incident response.

• As a Senior Full Stack Developer, you will play a key role in bringing the company’s product to market. • Transform approved UX/UI designs into responsive, intuitive, production-ready user interfaces. • Develop modern web applications using React, JavaScript/TypeScript, HTML, and CSS. • Ensure excellent usability, performance, accessibility, and responsiveness across the platform. • Work closely with designers and stakeholders to deliver a seamless user experience. • Design, develop, and maintain scalable backend services and application architecture. • Develop APIs and integrations with third-party platforms. • Design and manage databases and data structures. • Implement security, performance, and scalability best practices. • Troubleshoot and resolve application issues across the full technology stack. • Integrate AI services and APIs into the platform where appropriate. • Evaluate and recommend AI technologies that enhance product functionality. • Support the development of AI-driven workflows and user experiences. • Collaborate with stakeholders to identify opportunities for AI-enabled features. • Participate in technical architecture and product planning discussions. • Provide recommendations on frameworks, technologies, and development best practices. • Contribute to the ongoing evolution and scalability of the product.

Role Description As a Data Analyst, you will work at the intersection of business and data, helping transform manufacturing data into actionable insights. You will support decision-making by analyzing data from multiple systems and identifying opportunities for improvement in performance, costs, and efficiency. - Analyze manufacturing ERP-related data from multiple systems used across different sites - Translate business requirements into data and analytical solutions - Bridge communication between business insights and data engineering teams - Translate business needs into data requirements - Identify opportunities to maximize the use of existing data - Connect previously unrelated data sets for better analysis - Conduct profit and cost analysis of manufacturing processes - Combine and connect data from different sources for enhanced analysis Qualifications - Strong SQL skills - Experience in Python for data analysis - Experience working with ERP data models - Excellent communication skills and ability to work across technical and business teams - Proactive, self-driven, and able to work independently - Analytical mindset Requirements - Understanding of manufacturing processes and KPIs - Experience with order-to-cash processes (pricing, quoting, order flows) - Data visualization experience (Power BI or similar) - Familiarity with financial metrics definition and modeling - Experience in financial/profitability analysis Benefits - At Tieto, we believe in the power of diversity, equity, and inclusion. - We encourage applicants of all backgrounds, genders (m/f/d), and walks of life to join our team. - Our commitment to openness, trust, and diversity is at the heart of our mission to create digital futures that benefit businesses, societies, and humanity.

• Build Agent Architecture: Lead the design of agent architectures in .NET/C# (planning, memory, multi-step reasoning) and build ASP.NET Core backends with clean APIs for a React frontend. • Optimize the SDLC (Layer 1): Design agentic workflows to assist with coding, testing, and CI/CD across our .NET and React codebases; standardize internal AI developer tools. • Connect Enterprise Data (Layer 2): Build robust data ingestion, retrieval (RAG), and memory layers over internal systems. Design secure integrations (tool calling, MCP servers) so agents can take action across internal tools. • Deliver Customer Intelligence (Layer 3): Ship safe, customer-facing predictive features using customer data as context. Ensure strict data isolation, privacy, and multi-tenant security boundaries. • Set Standards & Cloud Practices: Establish org-wide standards for deploying, monitoring, and cost-optimizing agentic systems on Google Cloud Platform (GCP). • Mentor & Guide: Conduct technical reviews, make high-level decisions on model selection, and grow the engineering team's agentic systems expertise.