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описание
Capgemini Engineering is a global engineering services company that helps innovative organizations develop and apply engineering, digital, and software technologies across industries. It delivers R&D and engineering services, including solutions involving autonomous vehicles, robotics, AI, and software.
задачи
Design, build, and maintain Generative AI applications and agentic workflows supporting business-critical processes;
Develop AI-powered solutions using Large Language Models, Retrieval-Augmented Generation, tool integration, and multi-agent architectures;
Collaborate with business stakeholders and subject matter experts to understand requirements and translate them into scalable technical solutions;
Contribute to solution architecture, technical design decisions, and the definition of AI engineering standards;
Implement evaluation, monitoring, and observability capabilities for AI systems;
Design and develop APIs and backend services supporting AI applications and integrations;
Participate in deployment, testing, and continuous improvement throughout the AI solution lifecycle;
Ensure solution robustness through software engineering best practices, code quality standards, version control, and automated development workflows;
Create and maintain technical documentation, design artefacts, and knowledge-sharing materials;
Support demonstrations, workshops, and stakeholder presentations while helping promote AI adoption across the organisation;
Contribute to the evolution of AI engineering practices, frameworks, and reusable components;
Collaborate with data, engineering, and business teams to identify opportunities for innovation and business value creation through Generative AI.
требования
Degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related technical discipline;
2–5 Years of experience in Software Engineering, AI Engineering, or Data Engineering roles;
Demonstrated experience building applications using Generative AI technologies;
Strong Python programming skills and experience developing production-grade applications;
Experience designing and developing APIs using FastAPI or similar frameworks;
Good knowledge of SQL and data manipulation techniques;
Understanding of software engineering principles, system design, and repository organisation;
Experience using version control systems such as Git;
Familiarity with Retrieval-Augmented Generation architectures and context retrieval strategies;
Understanding of agentic design patterns, orchestration frameworks, and AI workflow concepts;
Knowledge of prompt engineering techniques, context management strategies, and LLM evaluation approaches;
Familiarity with ReAct, multi-agent workflows, tool calling, structured outputs, human-in-the-loop processes, and memory patterns;
Experience with at least one major cloud platform such as Azure, AWS, or Google Cloud Platform;
Understanding of containerisation technologies such as Docker;
Familiarity with dependency management tools such as Poetry, uv, pip, or equivalent;
Experience with CI/CD concepts and modern software development practices;
Strong communication and stakeholder management skills;
Ability to explain technical concepts effectively to technical and business audiences;