Principal Engineer – GenAI Platform

BrazilRemoteFull-timelead

EngineeringDevops Engineer

1 views0 saves0 applied

Apply Now

Quick Summary

Key Responsibilities

Lead the architecture, design, and implementation of enterprise-grade Generative AI solutions, including applications operating in regulated environments. Define scalable architectures using LLMs,

Technical Tools

EngineeringDevops Engineer

Take a leading technical role in shaping and delivering enterprise-scale Generative AI solutions within a global technology environment. As a Principal Engineer, you will define the architecture, engineering standards, and governance practices that enable teams to build reliable, secure, and scalable AI applications. You will work across advanced technologies such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, and model routing strategies. Partnering with product, engineering, and business stakeholders, you will turn complex requirements into production-ready solutions with measurable impact. You will also establish reusable frameworks, promote Responsible AI practices, and guide multidisciplinary teams through technical decisions. This is an opportunity to influence enterprise AI strategy while serving as a trusted technical leader and mentor.

As a Principal Engineer – GenAI Platform, you will lead the technical direction and implementation of Generative AI capabilities, ensuring solutions meet enterprise requirements for performance, scalability, security, and governance. Your responsibilities will include:

Lead the architecture, design, and implementation of enterprise-grade Generative AI solutions, including applications operating in regulated environments.
Define scalable architectures using LLMs, Retrieval-Augmented Generation (RAG), AI Agents, guardrails, and model selection and routing strategies.
Establish standards and frameworks for evaluating, monitoring, and governing Generative AI applications throughout their lifecycle.
Collaborate closely with product, engineering, and business teams to translate complex requirements into production-ready AI solutions.
Develop reusable frameworks, components, templates, and engineering best practices that can be adopted across multiple teams.
Lead technical reviews, influence architectural decisions, and provide technical guidance to engineers and data scientists.
Ensure AI solutions comply with Responsible AI principles, information security requirements, regulatory obligations, and model risk management practices.
Promote knowledge sharing and enablement, helping teams develop the skills and practices needed to operate and maintain AI solutions effectively.
Drive engineering excellence and encourage consistent approaches to building, deploying, and maintaining enterprise AI applications.

Requirements

~2 min read

The ideal candidate is a senior technical leader with deep expertise in Machine Learning Engineering and Generative AI, combining strong architectural capabilities with hands-on production experience. You should bring:

Advanced or fluent English proficiency, with the ability to communicate effectively in a global working environment.
Extensive experience in Machine Learning Engineering and/or LLM Engineering, particularly developing and operating production-grade solutions.
Practical experience with Retrieval-Augmented Generation (RAG), AI Agents, and Generative AI application architectures.
Strong knowledge of GenAI guardrails, model selection and routing, and strategies for managing LLM behavior and performance.
Experience implementing offline and online model evaluation, regression testing for AI applications, and human-in-the-loop validation processes.
Expertise in model monitoring, observability, and the ongoing evaluation of AI applications in production.
Experience working with cloud environments, particularly AWS and/or Databricks.
Knowledge of model and dataset versioning, experiment tracking, and MLOps practices.
Strong communication and stakeholder management skills, with the ability to influence multidisciplinary teams and senior stakeholders without relying on formal authority.
A practical, results-oriented approach to solving complex technical challenges and delivering scalable solutions.