AI & ML Services – Senior Machine Learning Engineer

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

Design, develop, deploy, and maintain scalable Machine Learning and Generative AI solutions that address complex business challenges. The role is responsible for building end-to-end ML systems, Agentic AI architectures, Retrieval-Augmented Generation (RAG) platforms, model serving APIs, and AI-enabled applications while partnering with business stakeholders, data scientists, product owners, and engineering teams. The Senior Machine Learning Engineer will drive MLOps best practices, model governance, cloud-native deployments, and production-grade AI solutions leveraging modern cloud and AI ecosystems.

Responsibilities

Machine Learning Engineering

• Design, build, test, deploy, and maintain Machine Learning 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.

Generative AI & Agentic AI

• Design and implement Agentic AI solutions using multi-agent architectures.
• Build AI agents using LangChain, LangGraph, CrewAI, AutoGen, or similar frameworks.
• Develop advanced prompt engineering strategies and orchestration workflows.
• Implement LLM-powered applications leveraging foundation models such as Gemini, GPT, Claude, or open-source alternatives.
• Design and deploy conversational AI solutions and enterprise copilots.

Retrieval-Augmented Generation (RAG)

• Design and implement end-to-end RAG architectures.
• Develop vector search solutions using embeddings and semantic search techniques.
• Integrate vector databases and enterprise knowledge repositories.
• Implement hybrid search, semantic retrieval, document chunking, ranking, and retrieval optimization.
• Evaluate and improve retrieval accuracy, grounding, and response quality.
• Fine-tune foundation models and domain-specific language models.
• Implement model evaluation frameworks and benchmarking methodologies.
• Define AI quality metrics including:
• Accuracy
• Relevance
• Groundedness
• Toxicity & Safety
• Conduct A/B testing and model comparison using industry-standard leaderboards and evaluation frameworks.
• Deploy and manage AI/ML solutions in GCP, Azure, AWS, or hybrid cloud environments.
• Implement CI/CD pipelines for Machine Learning and Generative AI workloads.
• Build automated model retraining and deployment workflows.Manage model lifecycle, versioning, governance, and auditability.
• Support production environments and ensure high availability and observability.

Required Technical Skills

Artificial Intelligence & Machine Learning

• Natural Language Processing (NLP)

Generative AI

• Agentic AI
• Prompt Engineering
• Embeddings
• Vector Databases
• AI Evaluation Frameworks

Programming Languages

• Python (Advanced)
• SQL (Advanced)
• TensorFlow
• PyTorch
• JAX
• Scikit-Learn

AI Frameworks

• LangChain
• LangGraph
• CrewAI (Preferred)
• AutoGen (Preferred)
• Amazon Web Services

Preferred Knowledge Areas

• BigQuery
• BigQuery ML
• Enterprise Data Warehousing
• Data Lake Architectures
• Spark
• Streaming Data Processing
• Batch Processing

MLOps & Automation

• Cloud Composer (Airflow)
• CI/CD for ML
• Infrastructure as Code
• Model Lifecycle Management

Containerization & Deployment

• Docker
• GKE
• REST APIs
• Model Serving Platforms

Monitoring & Observability

• Data Drift Monitoring
• Feature Drift Analysis
• Performance Monitoring
• Responsible AI Practices

Nice to Have

• Experience with Gemini ecosystem and Vertex AI Studio.
• Experience building enterprise AI copilots and agent-based workflows.
• Knowledge of Responsible AI, AI Governance, and AI Security.
• Experience with reinforcement learning and advanced AI evaluation methodologies.
• Contributions to AI/ML open-source projects.
• Exposure to multimodal AI solutions (text, image, speech, video).

Experience Level

Senior (7+ years of Data Science / Machine Learning experience)