4 geprüfte Deep Learning Jobs in Integration & Automatisierung

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Access to cutting-edge AI, agentic AI, and generative AI projects for leading global organisations - Continuous learning opportunities, certifications, and training across cloud, AI, and emerging technologies - Flexible working models that support the way you work best - International collaboration with multidisciplinary teams and leading AI experts - Clear career development opportunities towards Lead Engineer, AI Architect, or Principal Architect roles The Accenture Culture: Here, YOU are the catalyst for change Your unique combination of skills, personality, and aspirations sets you apart. When distinct talents, strengths, and perspectives merge, the result is nothing short of extraordinary: ideas that propel the world forward. This is why we champion diversity and personal development at Accenture.

Create a working environment where you truly thrive; with responsibilities that resonate deeply; with a workload that is perfectly aligned with your capabilities; and methods that are tailored to your objectives. How you wish to connect, further your development, and personally evolve rests in your hands. This is your career.

We are mere facilitators, helping you shape it exactly as you envision. Contact Us Any questions? Feel free to contact our recruiting team at In addition to exciting projects, we offer an attractive compensation package, flexible working hours, a modern environment, and many additional benefits.

You also have the opportunity to positively influence your annual income based on your individual performance. The annual gross salary for this position starts, depending on qualifications and experience, at €48,100. #LI-EU About Accenture Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries.

Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships.

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Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.

Zur Position

YOU ARE As a hands on AI Engineer, you will be at the heart of designing and building the components that make up advanced AI systems powering the modern enterprise. This is a deeply technical, hands-on engineering role — you will spend the majority of your time in the detailed design, development, integration, and testing of AI system components across classical machine learning, generative AI, and agentic systems, delivering these within active client engagements. You will take detailed architecture and design specifications and translate them into working, production-quality software components.

This means writing clean, well-structured code, making low-level design decisions within your assigned scope, and ensuring your components integrate reliably within the broader AI system. You will build and wire together the constituent parts of AI agent systems — including individual agent logic, tool integrations, skills, and memory components — and contribute to the development and integration of foundation and classical ML models into end-to-end pipelines. A hands-on curiosity for the open source ecosystem is essential in this role.

You will continuously evaluate, learn, and adopt relevant open source libraries and frameworks — such as those spanning agent orchestration, vector storage, model serving, and ML pipelines — selecting and applying the right ones for the problem at hand. Equally, you will configure, integrate, and operationalize third-party AI technologies and platform services, understanding their capabilities and constraints deeply enough to make them work reliably within the context of a larger enterprise system. You will engineer components with enterprise-grade qualities in mind, ensuring your work meets defined requirements across security, observability, governance, performance, and scalability.

You will write and maintain the technical artifacts that accompany your engineering work — including low-level design documents, component specifications, and integration contracts — ensuring your work is well-documented, testable, and handoff-ready. You will operate as a practitioner within cross-functional delivery teams alongside data engineers, ML engineers, and application developers, taking direction from lead and principal architects while contributing meaningfully to technical problem-solving and design discussions within your domain. This role is an opportunity to build deep, hands-on expertise across the AI engineering stack, develop strong software engineering fundamentals applied to cutting-edge AI systems, and grow toward a lead engineer or architect role over time.

THE WORK - Design, build, and configure individual agents — including their prompts, tools, and skills — and integrate them into multi-agent workflows - Implement agent orchestration logic that handles task handoffs, communication, and error recovery - Build evaluation harnesses and test suites that measure agent and component quality on metrics such as accuracy, relevance, and faithfulness, and share findings to inform design improvements - Integrate foundation models into applications, selecting the appropriate model and invocation pattern for each use case - Build and run model fine-tuning pipelines — including data preparation and training — to adapt models to specific business domains, applying working knowledge of transformer-based architectures - Build ingestion pipelines that parse, chunk, enrich, and index unstructured enterprise content for retrieval - Implement embedding generation, integrate vector databases, and develop retrieval components, including connectors and adapters that process unstructured content into end-to-end RAG pipelines - Build the logic that assembles prompts and manages what information is passed to the model within its context window - Implement memory components that store and recall conversational history and other relevant context - Implement input/output guardrails, content filtering, and defenses against prompt injection - Build PII detection and redaction components and integrate access controls for model and tool access - Implement versioning, audit logging, and lineage tracking, and maintain model documentation that keeps the system auditable - Instrument components with logging and tracing for requests, responses, token usage, and tool calls - Contribute to monitoring, alerting, and cost tracking that keep AI systems healthy in production - Continuously learn and apply new design patterns, technologies, and frameworks across the fast-evolving AI landscape, bringing fresh approaches to the components you build - Collaborate within cross-functional teams to clarify requirements and ensure your components meet stakeholder needs - Create and maintain clear technical documentation for the components you build, supporting troubleshooting and future development EDUCATION - • Bachelor s Degree in Computer Science, Computer Engineering, Data Science, or a related engineering discipline BASIC (REQUIRED) QUALIFICATION - Experience (work or coursework) in designing, coding, building advanced AI solutions using agentic, generative and classical AI/ML using at least one cloud vendor. - Experience (work or coursework in the Agentic, LLM and Generative AI space. - Experience (work or coursework) architecting and operationalizing LLM driven application architecture patterns. - Proficiency with coding and engineering, machine learning, deep learning and NLP solutions and applications. - Coding experience using python