AI Evaluation Specialist

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you'd like, where you'll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.

Your Role

As a Senior AI Engineer – Agentic AI & Document Intelligence at Capgemini , you will lead the design, development, and deployment of next-generation AI solutions that transform unstructured information into actionable business intelligence.

You will work at the intersection of Document AI, Agentic AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Cloud AI Platforms , helping organizations automate complex business processes and unlock value from enterprise data at scale.

This role req uires hands-on engineering expertise, strong software development skills, and experience delivering AI solutions from proof-of-concept through production deployment in enterprise environments.

Responsibilities & Scope

Document AI & Intelligent Document Processing

- Design and implement intelligent document processing solutions using OCR and Document AI technologies.

- Develop solutions capable of extracting, classifying, structuring, and validating information from unstructured and semi-structured documents.

- Build pipelines for processing business-critical documents such as contracts, invoices, forms, statements, claims, and enterprise records.

- Optimize document understanding workflows to improve accuracy, automation, and scalability.

- Collaborate with business and technical teams to identify opportunities for document intelligence automation.

Agentic AI Solutions

- Design and implement Agentic AI architectures capable of autonomous reasoning and task execution.

- Develop AI agents leveraging:

- Tool Calling

- Function Calling

- Multi-Agent Architectures

- Agent Orchestration

- Planning Frameworks

- Human-in-the-Loop Workflows

- Long-Term Context and Memory Management

- Define governance and operational controls for enterprise AI agents.

- Implement monitoring and quality controls for agent behavior and decision-making.

Generative AI & RAG Engineering

- Build production-ready LLM and Retrieval-Augmented Generation (RAG) solutions.

- Develop retrieval pipelines using:

- Embeddings

- Vector Databases

- Semantic Search

- Knowledge Bases

- Grounding Frameworks

- Design and optimize prompt engineering strategies.

- Implement evaluation frameworks to improve model accuracy, reliability, and relevance.

- Support enterprise knowledge management and AI-powered search experiences.

Cloud AI Platform Engineering

- Design, deploy, and support AI solutions within cloud environments.

- Leverage Google Cloud Platform (GCP) AI and Machine Learning services when applicable.

- Collaborate with Cloud and Data Engineering teams to build scalable AI architectures.

- Define deployment patterns, scalability strategies, and operational support models for AI applications.

- Support MLOps and AI lifecycle management activities.

Software Engineering & Platform Development

- Develop scalable AI applications using Python and modern software engineering practices.

- Build APIs, services, and integration layers supporting AI-powered capabilities.

- Implement observability, monitoring, logging, and performance optimization solutions.

- Ensure solutions meet enterprise requirements for security, reliability, and maintainability.

- Contribute to architecture reviews, technical standards, and AI engineering best practices.

Your Profile

- AI innovator – Passionate about emerging technologies and intelligent automation.

- Hands-on engineer – Comfortable building and deploying enterprise-grade solutions.

- Problem solver – Excels at translating complex business challenges into scalable AI solutions.

- Architectural thinker – Understands how AI solutions fit within broader enterprise ecosystems.

- Collaborative professional – Works effectively with business stakeholders, engineers, and architects.

- Continuous learner – Stays current with advancements in AI, GenAI, Agentic systems, and cloud technologies.

Qualifications & Preferred Skills

Required Skills

Document AI & OCR

- Strong hands-on experience with OCR and Intelligent Document Processing solutions.

- Demonstrated experience extracting, classifying, validating, and processing information from unstructured documents.

- Experience implementing enterprise Document AI workflows and document understanding capabilities.

Agentic AI

- Proven experience designing and implementing Agentic AI systems.

- Experience with:

- AI Agents

- Multi-Agent Architectures

- Agent Orchestration

- Planning Systems

- Memory Management

- Tool and Function Calling

- Human-in-the-Loop Workflows

- Ability to design autonomous workflows beyond traditional LLM implementations.

LLMs & RAG

- Strong expertise in:

- Large Language Models (LLMs)

- Prompt Engineering

- Retrieval-Augmented Generation (RAG)

- Embeddings

- Vector Databases

- Semantic Retrieval

- Grounding Strategies

- Experience building and deploying production-grade RAG solutions.

- Knowledge of model evaluation and optimization techniques.

Software Engineering

- Strong Python development experience.

- Experience designing and developing APIs and backend services.

- Knowledge of software engineering best practices and enterprise application development.

- Ability to build scalable, maintainable, and production-ready solutions.

Production AI Engineering

- Experience taking AI solutions through the complete lifecycle:

- Proof of Concept (POC)

- Architecture Design

- Development

- Deployment

- Monitoring

- Optimization

- Scaling

- Strong understanding of enterprise operational requirements for AI systems.

Preferred Skills

- Experience with Google Cloud Platform (GCP) .

- Hands-on experience with:

- Vertex AI

- Gemini

- Document AI

- Cloud Run

- GKE

- BigQuery

- Experience with Azure OpenAI or AWS AI/ML services.

- Familiarity with MLOps and AI Ops frameworks.

- Knowledge of data engineering and analytics ecosystems.

- Experience implementing enterprise AI governance and responsible AI practices.

- Exposure to data security, compliance, and privacy requirements.

What You'll Love

- Opportunity to work at the intersection of Product Management, Data, Analytics, and Marketing Technology.

- Exposure to enterprise-scale digital transformation initiatives.

- Collaboration with global Product, Engineering, Analytics, and Business teams.

- Access to certifications, learning programs, mentorship, and professional development opportunities.

- Innovative and collaborative work environment focused on continuous improvement.

- Career growth opportunities within Capgemini's Digital, Data, and Product Management practices.

Need to Know

Career Development in Spanish & English: Access to certifications, product management training, Agile academies, mentorship programs, and continuous learning opportunities.

Community Engagement: Participate in innovation challenges, product communities, knowledge-sharing sessions, volunteering programs, and networking initiatives across Capgemini.

About Capgemini

Capgemini is a global business and technology transformation partner, helping organizations accelerate their dual transition to a digital and sustainable world while creating tangible impact for enterprises and society. It is a responsible and diverse organization with more than 340,000 team members in over 50 countries.

With a strong heritage of more than 55 years, Capgemini helps clients unlock the value of technology through end-to-end services and solutions, leveraging capabilities in strategy and design, engineering, AI, generative AI, cloud, data, customer experience, and digital transformation.

At Capgemini Mexico, we are committed to fostering a diverse and inclusive workplace where everyone has equal opportunities. We welcome applications from all qualified candidates and evaluate them based on merit, skills, qualifications, and experience relevant to the role.