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Senior AI Engineer (Fullstack)

PwC Acceleration Centers · Mexico

Nouveau
Senior 🇬🇧 English
Python PyTorch TensorFlow Hugging Face Transformers Docker Kubernetes AWS GCP Azure SageMaker Vertex AI Databricks GitHub Actions MLflow LangChain LlamaIndex LangGraph LangFlow React Next.js vector databases OpenAI Anthropic Meta

Description du poste

About the role

We are looking for a Senior AI Engineer with full‑stack expertise to design, train and deploy advanced machine‑learning and large‑language‑model solutions in production. The role blends AI research, cloud‑native engineering and front‑end integration to deliver end‑to‑end intelligent applications.

Key responsibilities

  • Design, train and deploy ML models and LLMs, including Retrieval‑Augmented Generation pipelines.
  • Develop scalable, cloud‑native microservices using Docker, Kubernetes and CI/CD tools such as GitHub Actions and MLflow.
  • Build end‑to‑end AI applications that integrate with web, mobile or API‑first platforms.
  • Implement vector databases, orchestration frameworks (LangChain, LlamaIndex) and multi‑agent systems (LangGraph, LangFlow).
  • Deploy solutions on AWS, GCP or Azure using services like SageMaker, Vertex AI or Databricks.
  • Collaborate with front‑end teams (React, Next.js) and platform engineers to ensure seamless integration.
  • Translate complex business problems into robust, production‑ready AI solutions.

Required profile

  • Bachelor’s degree in Computer Science, AI, Machine Learning, Software Engineering or related field.
  • Minimum 3 years of professional experience building AI‑driven products.
  • Experience with responsible AI practices, bias mitigation and model governance is a plus.
  • Track record of contributing to open‑source projects or AI/ML publications is advantageous.

Required skills

  • Python programming.
  • ML frameworks: PyTorch, TensorFlow, Hugging Face Transformers.
  • Containerization and orchestration: Docker, Kubernetes.
  • Cloud platforms: AWS, GCP, Azure (SageMaker, Vertex AI, Databricks).
  • CI/CD and observability: GitHub Actions, MLflow.
  • Vector databases and RAG pipelines.
  • Orchestration tools: LangChain, LlamaIndex, LangGraph, LangFlow.
  • Front‑end technologies: React, Next.js.
  • Familiarity with LLM providers (OpenAI, Anthropic, Meta).

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PwC Acceleration Centers

Mexico