9 days ago
Aguascalientes, MexicoSenior
Responsibilities
- Design, develop, test, deploy, and maintain machine learning models and AI-powered applications.
- Build feature engineering, training, validation, inference, data preparation, and end-to-end ML pipelines.
- Develop production-ready model services, APIs, model-serving architectures, and inference endpoints.
- Design Generative AI, LLM, prompt engineering, RAG, vector search, semantic search, and hybrid search solutions.
- Build agentic AI and multi-agent systems using LangChain, LangGraph, tool calling, orchestration, and workflow automation.
- Implement model monitoring, performance tracking, drift detection, latency monitoring, and continuous improvement.
- Deploy and manage AI workloads using cloud-native platforms, containerization, Kubernetes, and MLOps automation.
- Collaborate with product owners, data scientists, data engineers, architects, and business stakeholders on AI use cases and success metrics.
- Participate in technical design reviews, architecture discussions, innovation initiatives, mentoring, and AI engineering best practices.
Requirements
- Strong expertise in machine learning and deep learning, including supervised learning, unsupervised learning, natural language processing, and transformer architectures.
- Experience building production-grade predictive models and AI solutions, including end-to-end RAG pipelines and AI evaluation or benchmarking.
- Hands-on experience with LLMs, embeddings, vector databases, semantic search, hybrid search, prompt engineering, and agentic AI systems.
- Advanced proficiency in Python and SQL, with strong software engineering and API development capabilities.
- Hands-on experience with TensorFlow, PyTorch, and JAX.
- Experience deploying, scaling, and operationally supporting AI/ML solutions on Google Cloud Platform, Azure, or AWS.
- Preferred experience with Vertex AI, Vertex AI Pipelines, Vertex AI Workbench, Model Registry, Feature Store, Vertex AI Studio, and Gemini-based solutions.
- Preferred experience with BigQuery, BigQuery ML, Cloud Storage, Dataflow, Apache Beam, Dataproc, Kubeflow, Cloud Composer, Docker, Kubernetes/GKE, and Cloud Run.
- Knowledge of AI governance, responsible AI practices, model monitoring, feature drift detection, data drift analysis, and model performance optimization.
Benefits
- Work on cutting-edge AI, machine learning, Generative AI, and enterprise-scale transformation initiatives.
- Access to Google Cloud AI, Vertex AI, Gemini, advanced ML platforms, certifications, technical academies, mentoring, and continuous learning.
- Career growth opportunities within Capgemini's Global Data & AI practice.
- Participation in AI communities, innovation labs, hackathons, knowledge-sharing forums, technical events, and volunteering initiatives.
- Access to career development resources in Spanish and English.
Tech Stack
Apache AirflowApache BeamAWSAzureDockerGoogle BigQueryGoogle Cloud PlatformKubernetesPythonPyTorchSQLTensorFlow
Categories
About Capgemini
Capgemini is a global IT services and consulting firm that delivers strategy, cloud, AI, software engineering, and managed services to large enterprises and public-sector clients. Founded in 1967 and headquartered in Paris, it is publicly traded on Euronext Paris and operates in 50+ countries. The group expanded its engineering capabilities by acquiring Altran in 2020, now operating as Capgemini Engineering.