Lead AI Engineer

Lead AI Engineer (3 Positions) Location : Brazil (Remote / Hybrid based on project needs)

Role Overview

We are seeking highly skilled Lead AI Engineers based in Brazil to design, develop, and deploy scalable AI and machine learning solutions across enterprise systems. The ideal candidates will have strong expertise in Generative AI , RAG architectures , LLMs , and hands-on experience with the Google Cloud Platform (GCP) AI ecosystem.

You will lead AI engineering initiatives, collaborate with cross-functional teams, and drive innovation in intelligent automation, personalization, and data-driven decision-making.

Key Responsibilities

Lead the design and development of scalable ML models , Generative AI , and RAG-based solutions.

Build end-to-end AI / ML pipelines using GCP tools : Vertex AI , Gemini , Vector Search , and Managed Notebooks.

Develop intelligent agents and orchestration using LangChain , LangGraph , Google ADK , and CrewAI .

Build, fine-tune, and deploy custom LLMs and multimodal AI models.

Own technical architecture, solution design, performance optimization, and deployment strategies.

Partner with data engineers, cloud teams, and product stakeholders to integrate AI at scale.

Implement MLOps best practices for versioning, monitoring, retraining, and governance.

Mentor junior engineers and contribute to AI engineering standards and frameworks.

Required Skills & Experience

6+ years of professional experience in AI / ML engineering , including leadership responsibilities.

Strong hands-on experience with Generative AI and RAG pipelines .

Proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).

Deep expertise in the GCP AI / ML stack , including :

Vertex AI

Gemini

Vector Search

AI Studio / AI APIs

Strong experience with agent-oriented frameworks :

LangChain

LangGraph

CrewAI

Google ADK

Experience deploying ML models into production using CI / CD and MLOps .

Strong understanding of APIs, data engineering fundamentals, cloud-native deployment, and microservices.

Excellent communication and ability to lead technical discussions with global teams.

Preferred Skills (Nice to Have)

Experience with multi-agent systems and advanced tool orchestration.

Knowledge of Kubernetes , Docker , and cloud-native architectures.

Understanding of AI security, compliance, and responsible AI frameworks.

Experience working in global, distributed engineering teams.

Education

Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field.