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