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)

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