Forward Deployed Engineer, Enterprise AI [LATAM]

You will embed closely with external partner engineering teams to deploy, test, and optimize large-scale AI solutions. This is a hands-on role where you will be responsible for ensuring smooth implementations across our platform.

You will tackle ambiguous deployment challenges head-on-like reducing latency, resolving interoperability issues, and building secure data connectors. You will then translate those technical hurdles into actionable feedback, working together with other engineering teams to iterate and refine the AI ecosystem. If you are a highly skilled engineer passionate about solving complex constraints and driving immediate, measurable impact, we encourage you to apply.

Responsibilities

Own Deployments: Embed directly with client engineering teams to manage the complete deployment lifecycle of AI solutions, from technical scoping to production handoff. Build Integrations & Workflows: Design and implement robust connections between Meta's AI platforms and complex client systems (e.g., CRMs, inventory systems, messaging infrastructure), configuring workflows to meet specific partner needs. System Optimization: Architect scalable solutions, analyze code quality, and resolve complex performance and latency bottlenecks in real-world AI workloads. Develop Reusable Tooling: Act as a force-multiplier by turning one-off deployment patterns into reusable software components and playbooks that accelerate future work. Implement Evaluation Frameworks: Build and configure rigorous testing frameworks tailored to client environments to ensure AI reliability, safety, and output quality. Provide Technical Leadership: Lead complex technical efforts and cross-functional workstreams while mentoring peer engineers.

Qualifications

Track record of setting technical direction for a team, driving consensus and successful cross-functional partnerships Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience Experience building maintainable and testable code bases, including API design and unit testing techniques 6+ years of programming experience in a relevant language or 3+ years of experience + PhD Experience building and scaling large-scale software systems, including successfully deploying complex AI applications from proof-of-concept into production environments Experience collaborating directly with external partners, enterprise clients, or cross-functional teams to unblock integrations and solve complex technical constraints Experience designing and implementing scalable solutions on major cloud platforms (e.g., AWS, GCP), including leveraging core cloud services (compute, storage, machine learning) to optimize AI workflows, reduce inference latency, and manage compute costs Experience implementing rigorous testing and evaluation frameworks for AI systems (e.g., establishing quality benchmarks, mitigating hallucinations, conducting safety reviews) Experience in regulated industries (financial services, insurance, healthcare) or navigating enterprise compliance requirements (SOC2, data residency, privacy frameworks) Demonstrated ability to independently lead technical projects and translate field challenges into actionable feedback (e.g., identifying integration gaps, proposing feature improvements) to guide internal engineering teams Ability to communicate effectively in both English and Portuguese, with strong written and verbal skills in each language, to support collaboration across global and local teams Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies