What you will do: 

• Desarrollar proyectos de IA enfocado al diseño 3D abordando tareas desde la conceptualización hasta la implementación y despliegue. 

• Diseñar e implementar modelos y soluciones automatizadas de IA utilizando Python y herramientas relacionadas. 

• Desarrollar sistemas de monitorización para controla el degradamiento de los modelos generados. 

• Creación de pipelines para el despliegue de modelos sobre Azure y automatizar el reentrenamiento de los mismos. 

• Trabajar con herramientas de diseño 3D, tanto paramétricas cómo no paramétricas como Freecad y Blender. 

• Aplicar algoritmos matemáticos en los sistemas de IA. 

• Evaluar y comparar modelos de inteligencia artificial. 

• Mantenerse actualizado sobre las últimas tendencias en IA y tecnologías Microsoft para integrar las mejores prácticas en los proyectos. 

Job description

We are looking for a motivated intern who is curious about cloud engineering, Infrastructure as Code, and automation. You should enjoy turning technical ideas into practical, maintainable solutions and be eager to learn from experienced cloud professionals.

Your main assignment will be to create a practical library of reusable Terraform modules for Azure. You will develop individual resource modules and combine them into larger, composable solutions, such as virtual networks with their related resources.

Beyond the modules themselves, you will build reusable GitHub Actions workflows, help establish well-organized repositories, and create a GitHub Copilot instruction template. Together, these assets will give the team a consistent foundation for future Azure deployments for our clients.

The internship will follow a sprint-based approach using Azure DevOps. At the start of each sprint, you will receive a clearly defined set of tasks and priorities. Regular check-ins, sprint reviews, and feedback will help you demonstrate results, adjust your approach, and steadily build your skills. This process will provide a transparent framework for tracking progress and evaluating your development throughout the internship.

You will work in a modern engineering environment using Azure DevOps for sprint planning, GitHub for code management, GitHub Actions for deployments, Terraform, GitHub Copilot, and Microsoft Azure. Guidance and regular feedback will be provided throughout the internship; prior experience with any of these tools is an advantage, but a strong willingness to learn is equally important.

Come join us

At Avanade, you will become part of a welcoming, collaborative community where people share knowledge, challenge ideas, and support one another. Our global perspective and strong connection to the Microsoft ecosystem create an environment where you can learn from diverse experiences, build meaningful relationships, and see how innovation takes shape in practice.

From your first day, you will be encouraged to ask questions, contribute your perspective, and take increasing ownership as your confidence grows. With regular guidance, open feedback, and opportunities to work alongside experienced colleagues, the internship is designed to be both challenging and supportive. You will leave with a clearer view of life in consulting, valuable professional experience, and a network of people invested in your development. If you are curious, open-minded, and ready to grow, we would be pleased to welcome you to Avanade. 

What You’ll Do

• Design and build reusable Terraform modules for Azure resources in line with agreed standards and best practices.

• Combine individual modules into larger, composable solutions, such as virtual networks with related networking resources.

• Create reusable GitHub Actions workflows for validating, testing, and deploying Terraform configurations.

• Organize and maintain GitHub repositories, including their structure, branching approach, documentation, and contribution guidelines.

• Develop and refine GitHub Copilot instructions that help the team generate consistent, maintainable infrastructure code.

• Work in sprints using Azure DevOps, taking ownership of assigned tasks and using sprint reviews and feedback to demonstrate progress and support ongoing evaluation.

• Test modules and workflows, provide demos, incorporate stakeholder feedback, and continuously improve quality and usability.

• Document and demonstrate your deliverables so the team can confidently adopt it for future Azure deployments.

Sede di assunzione: Roma, Milano, Bologna

Hai esperienza in progetti di Data Platform, Data Science o Artificial Intelligence come figura funzionale? Allora stiamo cercando te!  
 
In Avanade lavoriamo per fornire ai nostri partner le migliori soluzioni possibili sfruttando al meglio le tecnologie più all'avanguardia. In quest'ottica, stiamo potenziando il nostro team di business analyst in ambito Dati e Artificial Intelligence (Data&AI), capaci di apportare consistente expertise nell'introduzione, implementazione ed evoluzione di processi, strumenti e metodologie. 
 
Cosa farai: 
 
Come Business Analyst con esperienza in progetti in ambito di Data Platform e Data Science, ti specializzerai nella scrittura di analisi dei processi, analisi funzionali e modellazione di processi di business. Supporterai lo sviluppo e l'implementazione di soluzioni avanzate in ambito di Dati e di intelligenza artificiale (generative e non), migliorando processi e prodotti, e assicurando che il risultato sia pertinente, accurato e coerente con i requisiti condivisi dal cliente. Colmerai il divario tra business e tecnologia traducendo le esigenze aziendali in requisiti tecnici. 

Role Summary

Avanade India’s Data & AI practice is looking for an Agentic AI & Copilot Studio SME to lead client enablement and adoption for agent-based AI solutions built on Microsoft Copilot Studio and the wider Microsoft agent ecosystem. This is a training-led, client-facing role: the primary focus is designing and delivering structured skilling — instructor-led workshops, hands-on labs, train-the-trainer programmes and champion communities — for large organisations building and scaling agents.

What distinguishes this role from a platform-training role is agentic depth. You are expected to understand how agents actually work — orchestration and reasoning loops, tool and function calling, grounding and retrieval, memory and context handling, multi-agent patterns, evaluation and guardrails — and to teach Copilot Studio in that context rather than as a feature tour. You will build reference agents yourself, advise on live engagements, and be the person client teams escalate hard agent-design questions to.

Key Responsibilities

• Own the design and delivery of agentic AI and Copilot Studio skilling programmes for large enterprise audiences — curriculum design, instructor-led sessions, hands-on labs, self-paced content, assessments and certification pathways.
• Run train-the-trainer and champion-enablement programmes so client teams can sustain and scale agent development independently after the engagement.
• Teach agent design as a discipline, not a tool walkthrough — task decomposition, orchestration and reasoning patterns, tool/action design, grounding strategy, memory and context, human-in-the-loop checkpoints, failure modes and cost/latency trade-offs.
• Tailor content and depth to distinct audiences — business makers, pro-code engineering teams, and business and IT leadership.
• Build reference and demo agents in Copilot Studio — topics, generative answers, actions and connectors, agent flows, knowledge sources and multi-agent handoffs — to anchor training and seed client use cases.
• Design and maintain lab environments, sandbox tenants and exercise sets, keeping them current with the fast-moving agent-platform roadmap.
• Provide hands-on advisory on live engagements: agent design reviews, grounding and knowledge-source guidance, action/connector patterns, prompt and instruction design, testing and evaluation approaches, and go-live readiness.
• Advise on when to build in Copilot Studio versus a pro-code approach on Azure AI Foundry or an agent framework — and how the two coexist in a single client landscape.
• Establish evaluation and Responsible AI practice for agents: test sets and regression suites, groundedness and quality measurement, content safety, guardrails, human oversight and monitoring in production.
• Advise clients on agent governance and operating models — environment strategy, DLP policies, maker onboarding, publishing and approval workflows, agent lifecycle management, licensing considerations and usage analytics.
• Support use-case discovery workshops — identifying, qualifying and prioritising agent scenarios, and setting realistic expectations on what agents can and cannot do reliably.
• Act as the practice reference point for agentic AI enablement: build reusable training assets, playbooks, agent templates and accelerators, and mentor consultants joining the capability.
• Contribute to presales as the subject-matter expert — enablement approach, effort and duration estimates, demos and client-facing solution walkthroughs.