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Mastering AI Innovation

2 semanas atrás

Campinas, São Paulo, Brasil beBeeArtificial Tempo inteiro US$120.000 - US$160.000

Job Title: Senior AI Engineer

Overview:

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries.

We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

Role Description:

  • Design and deploy end-to-end agentic pipelines for structured and unstructured energy data;
  • Build and fine-tune LLM applications using RAG, vector databases, and knowledge graphs;
  • Develop models for contract intelligence, price forecasting, and energy portfolio optimization;
  • Deliver scalable APIs and AI services using FastAPI, Flask, or similar frameworks;
  • Integrate with cloud-native platforms (AWS, GCP) and modern data infrastructure;
  • Mentor peers and contribute to best practices in applied AI and ML engineering;

Required Skills:

  • 7+ years of professional software development experience;
  • 5+ years deploying machine learning systems in production;
  • 3+ years building with LLMs, both locally-hosted and API-based (OpenAI, Gemini, etc.);
  • Expertise in Python and libraries like PyTorch, TensorFlow, and Hugging Face;
  • Hands-on experience with agentic pipelines, RAG, vector search, and prompt engineering;
  • Deep familiarity with cloud platforms (AWS, GCP, Azure) and tools like Docker, Kubernetes;
  • Experience designing scalable microservices and REST APIs for AI inference workflows;
  • Excellent verbal and written communication – you'll regularly present to the C-suite;
  • Systems-level thinking with a product mindset;
  • Upper-Intermediate English level.

Benefits:

  • Competitive compensation;
  • Flexible working hours;
  • Exciting projects to propel your career forward;
  • Personalized growth roadmaps;
  • TechTalks and mentorship opportunities.

Requirements:

  • Professional software development experience;
  • Deploying machine learning systems in production;
  • Building with LLMs, both locally-hosted and API-based;
  • Python expertise and libraries like PyTorch, TensorFlow, and Hugging Face;
  • Agentic pipelines, RAG, vector search, and prompt engineering experience;
  • Cloud platforms (AWS, GCP, Azure) and tools like Docker, Kubernetes experience;
  • Scalable microservices and REST APIs for AI inference workflows experience;
  • Verbal and written communication skills;
  • Product mindset and systems-level thinking;
  • Upper-Intermediate English level.