📍 Job Overview

Job Title: Senior Google Cloud AI Engineer
Company: Devoteam
Location: Madrid, Madrid, Spain
Job Type: Full-time
Category: AI & Machine Learning Engineer
Date Posted: 2025-07-02
Experience Level: Mid-Senior level (5-10 years)
Remote Status: On-site

🚀 Role Summary

Develop and implement intelligent conversational agents using Vertex AI Agent Builder and other Google Cloud AI services.
Integrate large language models (LLMs) and retrieval-augmented generation (RAG) techniques to enhance natural language understanding and response generation.
Collaborate with UX designers and other engineers to create engaging user experiences.
Stay updated with the latest AI generative research and trends.

📝 Enhancement Note: This role requires a strong background in AI generative models, conversational agents, and Google Cloud AI services to drive innovation and deliver cutting-edge solutions.

💻 Primary Responsibilities

Agent Development: Design, develop, and implement intelligent conversational agents using Vertex AI Agent Builder and other relevant tools.
LLM Integration: Integrate LLMs from Google Cloud and third-party providers to improve natural language understanding and response generation in conversational agents.
RAG Implementation: Implement retrieval-augmented generation techniques to connect conversational agents with external data sources and provide accurate, contextualized responses.
Flow Orchestration: Utilize LangChain and LangGraph to build complex conversation flows and orchestrate interactions between different AI components.
Performance Optimization: Optimize the performance and scalability of conversational agents on Google Cloud.
Prompt Engineering: Apply best practices in prompt engineering to enhance the quality and efficiency of interactions with LLMs.
Collaboration: Work closely with UX designers and other engineers to create intuitive and attractive user experiences.
Research & Stay Updated: Keep up-to-date with the latest AI generative research and trends to continuously improve conversational agent capabilities.

📝 Enhancement Note: This role involves a mix of technical development, collaboration, and research, requiring a well-rounded AI engineer with strong problem-solving skills and a passion for staying current with the latest AI trends.

🎓 Skills & Qualifications

Education: A Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related field. Relevant coursework or projects in AI, machine learning, or natural language processing.

Experience: Proven experience (5-10 years) in developing conversational agents or chatbots, with a solid understanding of AI generative models, LLMs, and RAG techniques. Experience with Google Cloud AI services and familiarity with Python programming are essential.

Required Skills:

Proven experience in developing conversational agents or chatbots.
Solid understanding of AI generative models, LLMs, and RAG techniques.
Experience with Vertex AI Agent Builder, Dialogflow, or similar platforms.
Strong Python programming skills.
Excellent communication and teamwork skills.

Preferred Skills:

Experience with Google Cloud Platform and its AI services.
Knowledge of natural language processing (NLP) techniques.
Experience developing applications with Google's large language models (LLMs), such as PaLM.
Google Cloud certifications.
Contributions to open-source projects related to AI generative models or conversational agents.

📝 Enhancement Note: While not required, experience with Google Cloud Platform and familiarity with NLP techniques can provide a significant advantage in this role, as they are directly applicable to the development of conversational agents using Google Cloud AI services.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

Agent Demonstrations: Include live demonstrations or videos showcasing your conversational agent projects, highlighting their features, user interactions, and performance.
Code Quality: Ensure your code is well-documented, follows best practices, and adheres to industry standards.
Data Integration: Demonstrate your ability to integrate external data sources and use retrieval-augmented generation techniques to improve response accuracy and contextualization.
User Experience: Showcase your understanding of user experience design principles by presenting visually appealing and intuitive conversational agent interfaces.

Technical Documentation:

Project Documentation: Document your project's architecture, data flow, and any challenges faced during development.
Code Comments: Include comments in your code to explain complex logic, algorithms, or design decisions.
Version Control: Demonstrate your proficiency in version control systems, such as Git, by including relevant repositories in your portfolio.

📝 Enhancement Note: A strong portfolio in this role should emphasize the development of conversational agents, showcasing your ability to integrate AI models, optimize performance, and create engaging user experiences.

💵 Compensation & Benefits

Salary Range: The estimated salary range for a Senior Google Cloud AI Engineer in Madrid, Spain is €70,000 - €90,000 per year, based on market research and regional adjustments for web development and AI engineering roles.

Benefits:

Competitive salary and benefits package.
Opportunities for professional growth and development.
A dynamic and innovative work environment.
The chance to work on cutting-edge AI projects and drive technological advancements.

Working Hours: The standard workweek is 40 hours, with flexibility for project deadlines and maintenance windows.

📝 Enhancement Note: The estimated salary range is based on market research and regional adjustments for AI engineering roles in Madrid, Spain. Actual salary may vary depending on factors such as experience, skills, and company-specific compensation structures.

🎯 Team & Company Context

🏢 Company Culture

Industry: Devoteam operates in the technology consulting and digital transformation sector, focusing on strategy, platforms, cybersecurity, and business transformation through technology.

Company Size: With over 12,000 employees across 25 countries in Europe, the Middle East, and Africa, Devoteam offers a large, diverse, and international work environment for web developers and AI engineers.

Founded: Devoteam was founded in 1993 and has since grown into a leading European technology consulting firm, with a strong focus on innovation and digital transformation.

Team Structure: The AI and machine learning team at Devoteam consists of experienced AI engineers, data scientists, and researchers, working collaboratively to develop and implement cutting-edge AI solutions for clients. The team follows an agile development methodology, with regular sprint planning and code reviews to ensure high-quality deliverables.

Development Methodology:

Agile/Scrum: The team follows Agile/Scrum methodologies for project management, with regular sprint planning, daily stand-ups, and iterative development cycles.
Code Review: Code reviews are an essential part of the development process, ensuring code quality, knowledge sharing, and collective code ownership.
CI/CD Pipelines: The team uses CI/CD pipelines to automate deployment processes, ensuring efficient and reliable delivery of AI solutions.

Company Website: Devoteam

📝 Enhancement Note: Devoteam's large, international team and focus on innovation provide ample opportunities for AI engineers to work on diverse projects, collaborate with talented professionals, and drive technological advancements in the AI and machine learning space.

📈 Career & Growth Analysis

AI Engineering Career Level: This role is at the senior level in the AI engineering career path, focusing on the development and implementation of intelligent conversational agents using Google Cloud AI services. The role requires a deep understanding of AI generative models, LLMs, and RAG techniques, as well as strong problem-solving skills and the ability to collaborate effectively with cross-functional teams.

Reporting Structure: The Senior Google Cloud AI Engineer reports directly to the AI Engineering Manager and works closely with UX designers, other AI engineers, and data scientists to develop and implement AI solutions.

Technical Impact: The role has a significant impact on the development and deployment of intelligent conversational agents, driving innovation in AI and machine learning and enhancing user experiences for clients.

Growth Opportunities:

Technical Leadership: As a senior AI engineer, there are opportunities to mentor junior team members, lead technical projects, and contribute to the development of AI engineering best practices within the organization.
Architecture Decisions: With experience and expertise, the role may involve making critical architecture decisions that shape the direction of AI projects and influence the company's AI strategy.
Emerging Technology Adoption: The role offers the opportunity to stay updated with the latest AI trends and research, allowing the engineer to drive the adoption of emerging technologies within the organization.

📝 Enhancement Note: This role provides ample opportunities for career growth and development, with a focus on technical leadership, architecture decisions, and the adoption of emerging AI technologies.

🌐 Work Environment

Office Type: Devoteam's Madrid office is a modern, collaborative workspace designed to foster innovation and creativity. The office features open-plan workspaces, meeting rooms, and breakout areas, encouraging teamwork and knowledge sharing.

Office Location(s): Devoteam's Madrid office is located at Calle Alcalá 44, Centro, 28014 Madrid, Spain.

Workspace Context:

Collaborative Environment: The office features open-plan workspaces, encouraging collaboration and communication between team members.
Development Tools: The office is equipped with state-of-the-art development tools, multiple monitors, and testing devices to support AI engineering projects.
Cross-functional Collaboration: The office is designed to facilitate cross-functional collaboration between AI engineers, UX designers, data scientists, and other teams within the organization.

Work Schedule: The standard workweek is 40 hours, with flexibility for deployment windows, maintenance, and project deadlines. The office operates from Monday to Friday, with core hours between 9:00 AM and 6:00 PM.

📝 Enhancement Note: Devoteam's collaborative work environment, state-of-the-art development tools, and flexible work schedule provide an ideal setting for AI engineers to thrive, innovate, and drive technological advancements.

📄 Application & Technical Interview Process

Interview Process:

Technical Assessment: The first step in the interview process involves a technical assessment, focusing on the candidate's understanding of AI generative models, LLMs, RAG techniques, and their ability to develop conversational agents using Google Cloud AI services.
System Design Discussion: The second step involves a system design discussion, where the candidate is asked to describe their approach to designing and implementing a complex AI system, demonstrating their problem-solving skills and technical expertise.
Cultural Fit Assessment: The third step involves an assessment of the candidate's cultural fit, focusing on their communication skills, teamwork, and ability to collaborate effectively with cross-functional teams.
Final Evaluation: The final step in the interview process involves a comprehensive evaluation of the candidate's technical skills, problem-solving abilities, and cultural fit, leading to a hiring decision.

Portfolio Review Tips:

Agent Demonstrations: Highlight your ability to develop and implement intelligent conversational agents using Vertex AI Agent Builder and other Google Cloud AI services.
Data Integration: Showcase your proficiency in integrating external data sources and using retrieval-augmented generation techniques to improve response accuracy and contextualization.
User Experience: Demonstrate your understanding of user experience design principles by presenting visually appealing and intuitive conversational agent interfaces.
Code Quality: Ensure your code is well-documented, follows best practices, and adheres to industry standards.

Technical Challenge Preparation:

Agent Development: Familiarize yourself with the latest features and capabilities of Vertex AI Agent Builder and other relevant Google Cloud AI services.
LLM Integration: Brush up on your knowledge of LLMs and their integration with conversational agents, focusing on improving natural language understanding and response generation.
RAG Techniques: Review your understanding of retrieval-augmented generation techniques and their application in conversational agents.
Prompt Engineering: Study best practices in prompt engineering to enhance the quality and efficiency of interactions with LLMs.

ATS Keywords: (See the comprehensive list of ATS keywords at the end of this document)

📝 Enhancement Note: The interview process for this role focuses on assessing the candidate's technical expertise in AI generative models, LLMs, and RAG techniques, as well as their ability to collaborate effectively with cross-functional teams and adapt to the company's culture.

🛠 Technology Stack & Web Infrastructure

AI & Machine Learning Technologies:

Vertex AI Agent Builder: The primary tool for developing and implementing intelligent conversational agents using Google Cloud AI services.
Large Language Models (LLMs): LLMs from Google Cloud and third-party providers, such as PaLM, are used to enhance natural language understanding and response generation in conversational agents.
Retrieval-Augmented Generation (RAG): RAG techniques are employed to connect conversational agents with external data sources and provide accurate, contextualized responses.
LangChain & LangGraph: These tools are used to build complex conversation flows and orchestrate interactions between different AI components.

Google Cloud Platform:

Google Cloud AI Services: The role involves working with various Google Cloud AI services, such as Natural Language API, Speech-to-Text, Text-to-Speech, and Translation API, to enhance the functionality and user experience of conversational agents.
Google Cloud Functions: Serverless computing platform for building and connecting cloud services and APIs.
Google Cloud Storage: Object storage service for storing and retrieving any amount of data at any time.

📝 Enhancement Note: The technology stack for this role is centered around Google Cloud AI services, with a focus on Vertex AI Agent Builder, LLMs, and RAG techniques for developing and implementing intelligent conversational agents.

👥 Team Culture & Values

AI Engineering Values:

Innovation: Devoteam values innovation and encourages AI engineers to stay updated with the latest AI trends and research, driving technological advancements in the field.
Collaboration: The company fosters a culture of collaboration, with AI engineers working closely with UX designers, data scientists, and other teams to develop and implement AI solutions.
Quality: Devoteam is committed to delivering high-quality AI solutions that meet the needs of its clients and exceed their expectations.
Customer Focus: The company places a strong emphasis on understanding and addressing the unique needs of its clients, ensuring that AI solutions are tailored to their specific requirements and challenges.

Collaboration Style:

Cross-functional Integration: AI engineers work closely with UX designers, data scientists, and other teams to ensure that AI solutions are intuitive, user-friendly, and effective in addressing client needs.
Code Review Culture: The team follows a code review culture, ensuring that code quality, knowledge sharing, and collective code ownership are prioritized.
Peer Programming: AI engineers often engage in peer programming sessions to share knowledge, learn from one another, and improve their skills.

📝 Enhancement Note: Devoteam's AI engineering team values innovation, collaboration, quality, and customer focus, fostering a dynamic and engaging work environment for AI engineers to thrive and drive technological advancements.

⚡ Challenges & Growth Opportunities

Technical Challenges:

LLM Integration: Integrating LLMs from Google Cloud and third-party providers with conversational agents to improve natural language understanding and response generation can be complex and challenging, requiring a deep understanding of AI generative models and LLMs.
RAG Implementation: Implementing retrieval-augmented generation techniques to connect conversational agents with external data sources and provide accurate, contextualized responses can be technically demanding, requiring strong problem-solving skills and a solid understanding of data management and integration.
User Experience: Designing visually appealing and intuitive conversational agent interfaces that meet user expectations and enhance the overall user experience can be challenging, requiring a strong understanding of user experience design principles and a keen eye for detail.
Performance Optimization: Optimizing the performance and scalability of conversational agents on Google Cloud can be complex, requiring a deep understanding of cloud architecture, resource management, and AI model optimization techniques.

Learning & Development Opportunities:

AI Skill Advancement: The role offers ample opportunities for AI engineers to advance their skills in AI generative models, LLMs, and RAG techniques, as well as other emerging AI technologies.
Conference Attendance: Devoteam encourages its AI engineers to attend industry conferences and events, providing them with the opportunity to learn from leading experts in the field, network with peers, and stay updated with the latest AI trends and research.
Certification & Community Involvement: The company supports its AI engineers in obtaining relevant certifications and engaging with online communities, such as Kaggle, Stack Overflow, and GitHub, to expand their knowledge and skills.

📝 Enhancement Note: The technical challenges and learning opportunities in this role provide AI engineers with the chance to grow professionally, stay current with the latest AI trends, and drive technological advancements in the field.

💡 Interview Preparation

Technical Questions:

AI Generative Models: Brush up on your knowledge of AI generative models, LLMs, and RAG techniques, and be prepared to discuss their application in developing conversational agents.
Google Cloud AI Services: Familiarize yourself with the latest features and capabilities of Google Cloud AI services, such as Vertex AI Agent Builder, Natural Language API, Speech-to-Text, Text-to-Speech, and Translation API.
System Design: Prepare for system design questions that focus on your ability to design and implement complex AI systems, demonstrating your problem-solving skills and technical expertise.

Company & Culture Questions:

AI Engineering Culture: Research Devoteam's AI engineering culture and be prepared to discuss how your values and work style align with the company's.
Collaboration & Teamwork: Prepare for questions that assess your ability to collaborate effectively with cross-functional teams, demonstrating your communication skills, teamwork, and adaptability.
AI Project Impact: Be ready to discuss the potential impact of your AI projects on the company's clients and the broader AI engineering community.

Portfolio Presentation Strategy:

Agent Demonstrations: Highlight your ability to develop and implement intelligent conversational agents using Vertex AI Agent Builder and other Google Cloud AI services, focusing on their features, user interactions, and performance.
Data Integration: Showcase your proficiency in integrating external data sources and using retrieval-augmented generation techniques to improve response accuracy and contextualization.
User Experience: Demonstrate your understanding of user experience design principles by presenting visually appealing and intuitive conversational agent interfaces.

📝 Enhancement Note: The interview process for this role focuses on assessing the candidate's technical expertise in AI generative models, LLMs, and RAG techniques, as well as their ability to collaborate effectively with cross-functional teams and adapt to the company's culture.

📌 Application Steps

To apply for this Senior Google Cloud AI Engineer position at Devoteam:

Customize Your Portfolio: Tailor your portfolio to showcase your conversational agent projects, highlighting your ability to develop and implement intelligent conversational agents using Vertex AI Agent Builder and other Google Cloud AI services.
Optimize Your Resume: Highlight your relevant AI engineering skills, experience, and achievements, focusing on your proficiency in AI generative models, LLMs, and RAG techniques.
Prepare for Technical Challenges: Brush up on your knowledge of AI generative models, LLMs, RAG techniques, and Google Cloud AI services, focusing on the technical aspects of the role.
Research the Company: Familiarize yourself with Devoteam's AI engineering culture, values, and recent projects, and be prepared to discuss how your skills and experience align with the company's needs and goals.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and web development/server administration industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

ATS Keywords:

Programming Languages:

Python
JavaScript
SQL
Bash

Web Frameworks & Libraries:

React
Angular
Vue.js
Node.js
Express.js
Flask
Django

Server Technologies:

Apache
Nginx
Tomcat
JBoss
WebSphere
GlassFish
Google Cloud Platform
Amazon Web Services (AWS)
Microsoft Azure

Databases:

MySQL
PostgreSQL
MongoDB
Redis
Cassandra
Oracle
SQL Server
MariaDB

Tools & Methodologies:

Git
Docker
Kubernetes
Jenkins
Ansible
Puppet
Chef
Terraform
AWS CloudFormation
Google Cloud Deployment Manager
Azure Resource Manager
Agile
Scrum
Kanban
Waterfall
DevOps
CI/CD
Infrastructure as Code (IaC)
Serverless Architecture
Microservices Architecture
Containerization
Virtualization
Cloud-Native Applications
AI & Machine Learning
Natural Language Processing (NLP)
Deep Learning
Reinforcement Learning
Neural Networks
Large Language Models (LLMs)
Retrieval-Augmented Generation (RAG)
Conversational Agents
Chatbots
Voice User Interface (VUI)
Virtual Assistant
AI Ethics
Data Privacy
Cybersecurity
Access Control
Identity & Access Management (IAM)
Network Security
Disaster Recovery
Business Continuity
High Availability
Load Balancing
Content Delivery Networks (CDNs)
Caching
Web Performance Optimization
Accessibility
Responsive Design
Progressive Web Apps (PWAs)
Mobile-First Development
Cross-Browser Compatibility
Web Accessibility Initiative (WAI) Compliance
Search Engine Optimization (SEO)
Web Analytics
A/B Testing
User Experience (UX) Design
User Interface (UI) Design
Graphic Design
Wireframing
Prototyping
User Research
Usability Testing
Quality Assurance (QA)
Software Development Life Cycle (SDLC)
Agile Software Development
Waterfall Software Development
Extreme Programming (XP)
Test-Driven Development (TDD)
Behavior-Driven Development (BDD)
Domain-Driven Design (DDD)
Microservices Architecture
Event-Driven Architecture
Serverless Architecture
Cloud-Native Architecture
API Design
RESTful APIs
GraphQL
gRPC
WebSockets
WebRTC
Web APIs
Mobile APIs
Backend Development
Frontend Development
Full-Stack Development
Web Development
Server Administration
System Administration
DevOps Engineering
Site Reliability Engineering (SRE)
Technical Writing
Technical Documentation
Technical Blogging
Public Speaking
Mentoring
Coaching
Leadership
Project Management
Product Management
Scrum Master
Product Owner
Technical Specialist
Technical Lead
Technical Architect
Technical Evangelist
Technical Trainer
Technical Consultant
Technical Support
Technical Troubleshooting
Technical Problem Solving
Technical Analysis
Technical Design
Technical Review
Code Review
Pair Programming
Refactoring
Code Optimization
Algorithms
Data Structures
Computer Science Fundamentals
Software Engineering Principles
Software Design Patterns
Software Architecture
Software Development Best Practices
Software Testing
Software Quality Assurance
Software Deployment
Software Maintenance
Software Upgrades
Software Patching
Software Configuration Management
Configuration Management Database (CMDB)
IT Service Management (ITSM)
ITIL
COBIT
ISO/IEC 27001
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Senior Google Cloud AI Engineer