AI Engineer

Senior AI Engineer to join our AI & Data team and lead the design, development, and deployment of cutting-edge Artificial Intelligence solutions. Build production-grade AI systems that go beyond experimentation.

Sobre la empresa

We are a global digital product development company that helps organizations launch, scale, and evolve innovative technology solutions. Our teams work alongside startups, scale-ups, and enterprise organizations to build impactful digital products.

Sobre el rol

We are looking for a Senior AI Engineer to join our AI & Data team and lead the design, development, and deployment of cutting-edge Artificial Intelligence solutions. This role is ideal for an experienced engineer who combines deep expertise in Large Language Models (LLMs), intelligent agents, and Generative AI with strong software engineering fundamentals.

Responsabilidades

• Design and implement LLM-powered solutions, including RAG architectures, AI copilots, and multi-agent systems
• Fine-tune, evaluate, and deploy both open-source and proprietary foundation models
• Develop end-to-end AI-powered features in collaboration with product and engineering teams
• Build scalable APIs and backend services
• Own the complete lifecycle of AI solutions
• Collaborate with DevOps teams
• Implement observability, performance monitoring, logging, and error tracking
• Evaluate emerging AI frameworks and tools
• Participate in architecture discussions
• Mentor other engineers
• Partner with stakeholders to identify business value

Requisitos

• Designing and deploying LLM-powered solutions, including RAG architectures, AI copilots, and multi-agent systems for real-world business applications
• Building scalable backend services and AI-powered APIs using Python, FastAPI, Django, or similar frameworks in production environments
• Leveraging vector databases, embedding strategies, and prompt engineering techniques to develop reliable and high-performing Generative AI solutions
• Deploying, monitoring, and operating AI workloads on cloud platforms such as AWS, GCP, or Azure using Docker, Kubernetes, and Terraform
• Implementing AI evaluation, observability, and MLOps practices to ensure performance, reliability, and continuous improvement of production AI systems.

Deseables

• Familiarity with MLOps practices and model deployment workflows
• Strong software engineering practices including testing, code reviews, version control, and modular architecture

Beneficios

• Access to internal learning platforms, mentorship programs, certifications, and conferences
• Learning bonus to support professional development
• Opportunities to lead cross-functional initiatives
• Flexible remote work environment and Workation program
• Wellness benefits including mental health support and fitness initiatives
• Access to modern tools and technologies
• Referral bonus program and team recognition initiatives
• Collaborative, multicultural, and innovation-driven culture
• Long-term career growth opportunities