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Solutions Architect, Model Builder - LATAM

CLT (Efetivo)Presencial (Local)São Paulo-SPEmpresa Confidencial (Cadastre-se)

* Salário: R$ 3.000 a R$ 6.000 por mês (estimado)

* O valor exibido é uma estimativa calculada com base em dados públicos e referências do mercado. Não garantimos que este seja o salário oferecido para esta vaga específica.

Área: Tecnologia da Informação

Nível: Junior

Join Confidencial (Apenas para Cadastrados) as a Solutions Architect to help LATAM build culturally-nuanced LLMs and empower local developers to build and deploy next-generation agentic AI applications. Collaborate with premier startups, research labs and ISVs to develop the next generation components of the AI-native systems. By mastering Confidencial (Apenas para Cadastrados)’s core technologies—NIM, NeMo Framework, Dynamo, and Nemo Agent Toolkit—you will guide partners through the complexities of performance optimization and production-grade deployment. As a trusted advisor, you’ll transform raw LLM capabilities into high-performance, industry-focused enterprise agents. At Confidencial (Apenas para Cadastrados), we work as a unified front. You will collaborate daily with our Account Managers, DevRel leads, and Marketing experts to turn bold AI visions into regional realities.

What you'll be doing:

  • Localize the future: Fine-tune LLMs to speak the authentic language of specific regions and industries.

  • Develop and optimize training and inference workflows with partners and collaborate with internal Confidencial (Apenas para Cadastrados) development teams to improve our software stack

  • Build sophisticated agentic systems featuring multi-agent coordination, long-horizon reasoning, and sophisticated planning frameworks.

  • Develop full-scale solutions, including domain-specific enterprise agents and high-performance retrieval pipelines (RAG) spanning various data sources.

  • Optimize inference performance by bringing to bear GPU-accelerated frameworks and the full Confidencial (Apenas para Cadastrados) AI infrastructure stack.

  • Build hands-on PoCs and reference architectures that serve as the blueprint for production-grade generative AI pipelines.

  • Partner with high-growth startups and Enterprise ISVs to embed Confidencial (Apenas para Cadastrados)’s software stack into their core platforms, slashing the time to market for production-grade AI.

  • Fuel partner innovation through hands-on developer enablement and thorough architectural reviews, turning sophisticated AI visions into production realities.

  • Scale global expertise by crafting reusable assets and documentation that help field teams deploy agentic AI at scale.

What we need to see:

  • BS/MS/PhD in Computer Science, Electrical Engineering, AI/ML, or equivalent experience.

  • 5+ years of experience in deep learning, machine learning, or distributed AI systems.

  • Strong programming and debugging experience in Python, C/C++, and Linux environments.

  • Background in using deep learning libraries like PyTorch or TensorFlow.

  • Hands-on experience building LLM and generative AI applications.

  • Experience working with agentic or multi-agent AI systems employing frameworks such as: LangGraph, LlamaIndex, CrewAI, LangChain, or OpenAI Agents SDK or similar orchestration frameworks

  • Experience building tool-using AI agents that interact with APIs, databases, and enterprise systems.

  • Ability to rapidly prototype AI applications and build scalable GPU-accelerated architectures.

  • Excellent interpersonal skills and the ability to collaborate with engineering teams, partners, and executive collaborators.

Ways to Stand Out from the Crowd:

  • Experience working with Confidencial (Apenas para Cadastrados) GPUs and AI software, such as Confidencial (Apenas para Cadastrados) NIM, NeMo Framework, NeMo Retriever, and NeMo Agent Toolkit.

  • Experience with LLM evaluation frameworks, benchmarking systems, and safety guardrails for agentic workflows.

  • Experience optimizing reasoning-focused LLMs through timely engineering, quantization, or benchmarking.

  • Familiarity with Kubernetes/OpenShift, CI/CD automation, and cloud-native deployment patterns for AI systems.

  • Experience with parallel or distributed computing environments and AI workloads optimized for GPUs.