Senior ML Engineer — Generative AI, RAG & MLOps Leader

Capgemini; México; On-site; MXN 800,000 - 1,200,000; Full time

Job summary

Capgemini is seeking a Senior Machine Learning Engineer to design, build, deploy, and maintain end-to-end ML systems and AI-enabled applications. You will drive MLOps, governance, and scalable cloud deployments while collaborating with stakeholders and engineers.

The role focuses on generative AI, Agentic AI architectures, RAG platforms, and production-grade AI solutions across GCP, Azure, AWS, or hybrid clouds.

Qualifications

• Design, build, test, deploy, and maintain ML models and AI-powered applications.
• Develop scalable feature engineering, training, validation, and inference pipelines.
• Build reusable ML frameworks and services for model deployment and monitoring.
• Translate business requirements into measurable ML use cases and metrics.
• Collaborate with Data Scientists to operationalize predictive and generative AI models.
• Optimize model performance, scalability, reliability, and cost efficiency.

Responsibilities

• Design, build, test, deploy, and maintain ML models and AI-powered applications.
• Develop scalable feature engineering, training, validation, and inference pipelines.
• Build reusable ML frameworks and services for model deployment and monitoring.
• Translate business requirements into measurable ML use cases, success metrics, and technical solutions.
• Collaborate with Data Scientists to operationalize predictive and generative AI models.
• Optimize model performance, scalability, reliability, and cost efficiency.

Skills

Python

SQL

TensorFlow

PyTorch

JAX

Scikit-Learn

LangChain

LangGraph

CrewAI

AutoGen

AWS

GKE

REST APIs

Model Serving Platforms

CI/CD for ML

Docker

Cloud Platforms

BigQuery

Spark

Data Lake Architectures

Data Warehouse

Vertex AI Studio

Embeddings

Vector Databases

NLP

Responsible AI

AI Governance

AI Security

Tools

Docker

GKE

REST APIs

Vertex AI Studio

Job description

Capgemini is seeking a Senior Machine Learning Engineer to design, build, deploy, and maintain end-to-end ML systems and AI-enabled applications. You will drive MLOps, governance, and scalable cloud deployments while collaborating with stakeholders and engineers.

The role focuses on generative AI, Agentic AI architectures, RAG platforms, and production-grade AI solutions across GCP, Azure, AWS, or hybrid clouds.