Role Summary 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. Model Optimization & AI Evaluation




• Fine-tune foundation models and domain-specific language models.
• Implement model evaluation frameworks and benchmarking methodologies.
• Define AI quality metrics including:
• Accuracy
• Relevance
• Groundedness
• Hallucination Detection
• Toxicity & Safety
• Latency
• Cost Efficiency
• Conduct A/B testing and model comparison using industry-standard leaderboards and evaluation frameworks. Cloud AI & MLOps
• 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
• Machine Learning
• Deep Learning
• Natural Language Processing (NLP)
• Transformer Architectures
• Supervised & Unsupervised Learning
• Model Training & Optimization
• Feature Engineering
• Model Evaluation
• Fine-Tuning Techniques Generative AI
• Large Language Models (LLMs)
• Agentic AI
• Multi-Agent Architectures
• Prompt Engineering
• RAG Architectures
• Embeddings
• Vector Databases
• AI Evaluation Frameworks Programming Languages
• Python (Advanced)
• SQL (Advanced) Machine Learning Frameworks
• TensorFlow
• PyTorch
• JAX
• Scikit-Learn AI Frameworks
• LangChain
• LangGraph
• LlamaIndex
• CrewAI (Preferred)
• AutoGen (Preferred) Cloud Platforms
• Google Cloud Platform (Preferred)
• Microsoft Azure




• Amazon Web Services Preferred Knowledge Areas Vertex AI Ecosystem
• Vertex AI Pipelines
• Vertex AI Workbench
• Vertex AI Model Registry
• Vertex AI Feature Store
• Vertex AI Studio
• Gemini Models Data Architecture
• BigQuery
• BigQuery ML
• Cloud Storage
• Enterprise Data Warehousing
• Data Lake Architectures Data Engineering
• Apache Beam
• Google Cloud Dataflow
• Dataproc
• Spark
• Streaming Data Processing
• Batch Processing MLOps & Automation
• Kubeflow
• Cloud Composer (Airflow)
• CI/CD for ML
• Infrastructure as Code
• Model Governance
• Model Lifecycle Management Containerization & Deployment
• Docker
• Kubernetes
• GKE
• Cloud Run
• REST APIs
• Model Serving Platforms Monitoring & Observability
• Model Drift Detection
• Data Drift Monitoring
• Feature Drift Analysis
• Latency Monitoring
• 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) Education Bachelor's or Master's degree in:
• Computer Science
• Artificial Intelligence
• Data Science
• Machine Learning
• Software Engineering
• Related Technical Field Keywords for LinkedIn Search Mandatory
• Machine Learning
• Python
• LLM
• RAG
• LangChain
• LangGraph
• Vertex AI
• GCP
• TensorFlow OR PyTorch
• NLP
• Agentic AI Highly Preferred
• Gemini
• BigQuery
• Kubeflow
• Dataflow
• Apache Beam
• MLOps
• Feature Store
• Fine Tuning
• Vector Database
• Multi-Agent Systems

📌 AI & ML Services – Senior Machine Learning Engineer (México)
🏢 Capgemini
📍 México