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.