AI ML Developer

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 Machine Learning Engineer at Capgemini, you will be responsible for designing, developing, deploying, and maintaining advanced Artificial Intelligence and Machine Learning solutions that drive business innovation and operational excellence. You will work with multidisciplinary teams including Product Owners, Data Scientists, Data Engineers, Architects, and Business Stakeholders to transform complex business challenges into scalable AI-powered products. This role requires expertise in machine learning engineering, Generative AI, Agentic AI systems, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), MLOps, and cloud-native AI platforms. You will contribute to the development of production-grade AI solutions spanning predictive analytics, intelligent automation, conversational AI, and next-generation Generative AI applications. Responsibilities & Scope Machine Learning Engineering Design, develop, test, deploy, and maintain Machine Learning models and AI-powered applications. Build scalable feature engineering, training, validation, and inference pipelines. Develop production-ready model services, APIs, and AI-enabled application components. Implement model monitoring, performance tracking, and continuous improvement processes. Ensure reliability, scalability, and maintainability of ML solutions in production environments. Generative AI & LLM Solutions Design and implement enterprise Generative AI solutions leveraging Large Language Models (LLMs). Develop prompt engineering strategies and model optimization approaches. Build Retrieval-Augmented Generation (RAG) architectures for knowledge retrieval and intelligent search. Implement vector search solutions leveraging embeddings and vector databases. Evaluate and optimize AI solutions using established AI evaluation methodologies and benchmarks. Support customization and fine-tuning of foundation models. Agentic AI & Multi-Agent Systems Design and develop Agentic AI solutions utilizing: LangChain LangGraph Multi-Agent Architectures Agent Orchestration Frameworks Tool Calling Workflow Automation Build intelligent autonomous workflows that leverage reasoning, planning, memory, and external tools. Implement secure and scalable AI agent frameworks aligned with enterprise requirements. Data Engineering & AI Pipelines Develop robust data preparation and feature engineering pipelines. Design end-to-end ML workflows supporting: Data Ingestion Feature Creation Model Training Evaluation Deployment Monitoring Collaborate with data engineering teams to support scalable AI and analytics platforms. Ensure data quality, lineage, and reproducibility across AI initiatives. Cloud AI & MLOps Deploy and manage AI workloads within Google Cloud Platform or similar cloud environments. Implement MLOps practices and automation pipelines. Design CI/CD workflows supporting machine learning lifecycle management. Develop scalable model serving architectures and inference endpoints. Support containerized and cloud-native AI deployments. Collaboration & Innovation Partner with Product Owners and Business Stakeholders to translate business objectives into measurable AI use cases. Define acceptance criteria, success metrics, and performance objectives for AI solutions. Participate in architecture discussions, technical design reviews, and innovation initiatives. Mentor team members and contribute to AI engineering best practices. Your Profile AI innovator – Passionate about Machine Learning, Generative AI, and emerging technologies. Hands-on builder – Enjoys designing and developing production-grade AI solutions. Problem solver – Able to simplify complex challenges through data-driven approaches. Cloud-native engineer – Comfortable building scalable solutions in modern cloud environments. Collaborative professional – Thrives in cross-functional teams and agile environments. Continuous learner – Stays current with advancements in AI, ML, MLOps, and GenAI technologies. Qualifications & Preferred Skills Required Skills Machine Learning & Deep Learning Strong expertise in Machine Learning and Deep Learning techniques. Experience building production-grade predictive models and AI solutions. Solid understanding of: Supervised Learning Unsupervised Learning Deep Learning Natural Language Processing (NLP) Transformers Architecture Generative AI & RAG Hands-on experience with: Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) Embeddings Vector Databases Semantic Search Hybrid Search Prompt Engineering Experience implementing end-to-end RAG pipelines. Knowledge of AI evaluation methodologies and model benchmarking. Agentic AI Experience designing and implementing Agentic AI systems. Hands-on experience with: LangChain LangGraph Multi-Agent Architectures Agent Orchestration Frameworks Tool Integration Workflow Automation Programming & Frameworks Advanced proficiency in: Python SQL Hands-on experience with: TensorFlow PyTorch JAX Strong software engineering and API development capabilities. Cloud & Deployment Experience deploying AI/ML solutions on: Google Cloud Platform (preferred) Azure AWS Experience with production deployment, scaling, and operational support of AI systems. Preferred Skills Google Cloud & Vertex AI Experience leveraging: Vertex AI Vertex AI Pipelines Vertex AI Workbench Model Registry Feature Store Vertex AI Studio Experience customizing and deploying Gemini-based solutions. Data Engineering & Analytics BigQuery BigQuery ML Cloud Storage Dataflow Apache Beam Dataproc Enterprise Data Pipelines MLOps & Platform Engineering Kubeflow Cloud Composer (Airflow) CI/CD for ML Containerization (Docker) Kubernetes (GKE) Cloud Run Automated Model Deployment Model Operations Model Monitoring Feature Drift Detection Data Drift Analysis Latency Monitoring Model Performance Optimization AI Governance and Responsible AI Practices What You'll Love Opportunity to work on cutting-edge AI, Machine Learning, and Generative AI initiatives. Exposure to enterprise-scale AI transformation programs and emerging technologies. Access to Google Cloud AI, Vertex AI, Gemini, and advanced ML platforms. Collaboration with global Data, AI, Analytics, and Cloud experts. Continuous learning through certifications, technical academies, mentoring, and innovation programs. Career growth opportunities within Capgemini's Global Data & AI practice. Need to Know Career Development in Spanish & English: Access to AI, Machine Learning, Cloud, and Data Engineering certifications, mentorship programs, technical academies, and continuous learning opportunities. Community Engagement: Participate in AI communities, innovation labs, hackathons, knowledge-sharing forums, technical events, and volunteering 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, 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.