A Fortune 100 technology leader in Latin America was looking to accelerate AI deployment across its enterprise product lines. But traditional engineering teams weren’t equipped for the specialized demands of AI delivery. To achieve its innovation goals, the company needed a cross-functional, AI-native team to build, deploy, and scale production-ready AI solutions.

Problem Statement

As AI adoption spread across the enterprise landscape, a Fortune 100 tech giant in Latin America faced a critical challenge: traditional engineering teams lacked the depth and specialization to operationalize large language models (LLMs) and AI systems in production.

The company needed to move from AI experimentation to enterprise-scale execution fast.

Key Requirements Included:

• Fine-tuning and deploying LLMs with business-specific data
• MLOps pipelines for monitoring, drift detection, and retraining
• Retrieval-augmented generation (RAG) workflows
• GPU infrastructure and real-time inference
• Enterprise compliance and AI governance
• Precision-matched engineers without long onboarding timelines

Challenge

Although the company had strong engineering capabilities for web and cloud platforms, its teams weren’t designed for AI-first delivery. They struggled to:

• Integrate AI into core product pipelines
• Maintain feedback loops for self-learning agents
• Align security and compliance with rapidly evolving AI use cases
• Iterate fast without compromising governance or performance

With time-to-market pressures mounting and AI-native architectures adding complexity, the company hit a bottleneck. What it needed was an elite delivery team that could execute immediately without compromise on speed, compliance, or innovation.

Solution

The company partnered with Hyqoo, a global AI talent cloud platform, to build an AI-first delivery team with proven experience in enterprise-scale AI systems.

Hyqoo’s Approach

• Precision Mapping: Hyqoo worked with the client’s technical leaders in Latin America to define role requirements across AI engineering, MLOps, backend systems, and product management.
• Rapid Talent Deployment: Within 2 weeks, Hyqoo deployed a cross-functional team of pre-vetted AI professionals matched for domain expertise, tool familiarity, and enterprise readiness.
• Embedded Delivery Model: Hyqoo’s team integrated directly with client squads, aligned with agile cadences, compliance standards, and local regulatory requirements, while providing hands-on execution and knowledge transfer.

Roles Delivered

• 5 LLM Engineers with fine-tuning and LangChain expertise
• 2 MLOps Engineers for monitoring, retraining, and CI/CD pipelines
• 1 AI Product Manager to drive roadmap execution
• 2 Backend Developers with RAG and vector databases experience
• 1 Data Privacy & Compliance Engineer for governance alignment

Results & Impact

• 30% reduction in AI feature delivery cycle times
• 2 production launches of AI copilots within 45 days
• Full MLOps pipeline implemented for continuous optimization
• Compliant integration of AI features into customer-facing applications
• Internal enablement of engineering teams through joint delivery and documentation

Client Experience

“Hyqoo didn’t just fill roles; they built a team that executed from day one. Their ability to map our needs, onboard fast, and deliver consistently made all the difference. We were able to turn vision into production-ready AI features in record time.”

— Director of Product Engineering, Fortune 100 Tech Giant (Latin America)

Key Takeaways

• Building AI-first delivery teams is critical for enterprise-grade AI
• Traditional engineering structures aren’t designed for AI
• Speed and precision in AI hiring reduce risk and accelerate transformation
• Cross-functional delivery (AI + backend + MLOps + compliance) is key to enterprise AI success

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