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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Generative AI Software Engineer Specialist (all genders) - **Company:** Accenture GmbH - **Location:** Kronberg im Taunus, Germany - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Abstraction Layers, Application Programming Interfaces (APIs), Artificial Intelligence, Automated Storage and Retrieval Systems, C Sharp (Programming Language), Cloud Computing, Cloud Engineering, Computer Programming, Continuous Integration, Software Debugging, Python (Programming Language), Rapid Prototyping Process, OpenAI, Software Deployment, TypeScript, Data Logging, Retrieval-Augmented Generation, Open-source Models, Large Language Models, Multi-Agent Systems, Prompt Engineering, Google Vertex AI, Generative AI, Agentic-AI, Event Driven Architecture, Containerization, AI Platforms, Kubernetes, Information Technology, Production Code, Free and Open-Source Software, Claude, Observability for Autonomous Agents, Terraform, Automation Anywhere, Serverless Computing, Docker, Microservices - **Published:** October 14, 2026 - **Apply:** https://www.xing.com/jobs/kronberg-im-taunus-generative-ai-software-engineer-specialist-genders-158561318 ## About the Role * Demonstrated engineering experience with cloud-native systems - APIs, microservices, containerisation, serverless architectures. * Deep, production-proven expertise in agentic AI systems: agents, multi-agent orchestration, context engineering, RAG pipelines, and workflow automation. * Hands-on experience with leading AI platforms - Anthropic Claude, OpenAI, Google Vertex AI - and comfort building abstraction layers across multiple providers. * Strong programming skills in Python (primary) and at least one of Java, TypeScript, or C#; experience with evaluation tooling, logging, and agent observability stacks. * Production deployment experience: CI/CD, infrastructure-as-code (Terraform, Helm), monitoring, incident response, and debugging. * Fluency in English - able to run workshops, present to executives, and write technical documentation. * Bachelor's degree in Computer Science, Engineering, or equivalent; AI certifications or demonstrated agentic tooling experience is a plus. Bonus Points * Experience at the intersection of design and engineering - contributing to or building upon design systems, working alongside UX/product designers on AI-powered experiences. * Background as a Product Engineer, Solutions Architect, or Forward-Deployed AI Engineer in a consulting, agency, or product studio environment. * Familiarity with responsible AI principles: guardrails, content filters, human-in-the-loop design, bias evaluation, and fallback strategies. * Experience across multiple industries - retail, finance, healthcare, media - tailoring agentic workflows to sector-specific processes and compliance requirements. * A portfolio of shipped work: apps, agents, open-source contributions, hackathon projects, or startup experience that demonstrates creative problem-solving under constraints. * German language good to have, but not mandatory. ## Description You are an AI Native Product Engineer with demonstrated experience building cloud-native systems and shipping production-grade AI solutions. You combine hands-on engineering depth with the creativity and product intuition to shape solutions that make sense for real users and enterprises. You're as comfortable writing production code as you are whiteboarding system designs, facilitating a client workshop, or mapping a product roadmap. You thrive in ambiguity and act as a bridge between vision and execution. You do not treat AI as a toolset to learn on the side. It is your primary medium. You architect around large language models, retrieval systems, and multi-agent orchestration the way previous generations of engineers built around microservices - with rigour, craft, and a clear eye on what will actually work in production. You translate complex AI behaviour into tangible user experiences that non-technical stakeholders can understand and trust. The Work You will embed directly with clients as a technologist, product thinker, and trusted engineering partner. Working within cross-functional Song studio teams - alongside Product Designers, Experience Designers, Product Managers, and Strategists - you will lead the engineering of AI-native digital products from concept through to production. You will shape not only what gets built but how it gets built, setting the technical standard for AI-native delivery within the practice. This is not a back-office role. You will be in the room with clients, translating AI possibilities into product realities, and in the codebase, making those realities work. AI-Native Product Engineering * Product Orchestration: Partner with business and technical stakeholders to frame problems, align priorities, and translate needs into orchestrated AI workflows. * Agent Architecture & Engineering: Design and engineer enterprise-ready AI agents - including retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability. * Human-AI Experience Engineering: Translate complex AI behaviours (confidence states, reasoning traces, agentic actions, fallback flows) into transparent, controllable, and accessible user experiences - in close collaboration with Experience Designers and Product Designers. * AI Platform Integration: Build robust abstraction layers across AI providers (Anthropic Claude, OpenAI, Google Vertex AI, open-source models) to create portable, multi-provider pipelines that reduce client lock-in and maximise flexibility. * AI Design System Contributions: Contribute to AI-native component libraries and design system patterns - error states, human-in-the-loop interactions, progressive disclosure of AI reasoning - ensuring consistency and scalability across client products. Cloud-Native Engineering & Delivery * Scalable Systems: Apply containerisation (Kubernetes, Docker), microservices, serverless, and event-driven architectures to ship AI-native systems that perform reliably at enterprise scale. * Production Discipline: Own CI/CD pipelines, infrastructure-as-code (Terraform, Helm), monitoring, and debugging. Deploy to production and take responsibility for what you ship. * Rapid Prototyping: Build working prototypes and proof-of-concept systems that close the gap between design vision and technical reality - using AI tooling actively to accelerate your own delivery. Client Partnership & Practice Building * Trusted Advisor: Serve as the primary technical voice in client engagements - framing problems, aligning technical trade-offs with business goals, and communicating clearly to both engineers and executive stakeholders. * Workshop Facilitation: Lead design sprints, co-creation sessions, and code-with workshops. Bring clients into the process, not just the outcome. * Measurement & Improvement: Define and track evaluation frameworks for agent accuracy, latency, cost, and safety. Use data to drive continuous improvement of deployed AI systems. * Knowledge Amplification: Document reusable patterns, architectural decision records, and best practices.