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 (México)
🏢 Capgemini
📍 México