AI Native Engineer - Senior Associate

Xtillion•San Juan, PR

About The Position

About the Role We’re looking for a Senior AI Engineer to join our team. In this role, you’ll design, build, and deploy intelligent systems that solve real business problems; from LLM-powered applications and retrieval pipelines to predictive models and data-driven automation. You’ll bring together software engineering rigor and applied AI expertise to deliver production-grade solutions in client environments. Whether your background is in software engineering, data science, machine learning engineering, or a blend of disciplines, this role is for builders who want to work at the frontier of applied AI: translating complex business challenges into scalable, reliable, AI-native systems that create measurable outcomes.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a related field
4+ years of professional experience in software engineering, data science, machine learning engineering, or a related technical discipline
Demonstrated experience building and deploying AI-enabled or data-intensive production systems
Experience with modern backend or data frameworks (e.g., FastAPI, Node.js)
Strong experience with SQL and relational or analytical data systems
Experience working in cloud environments (e.g., AWS, Azure, GCP)
Familiarity with Git-based version control, CI/CD pipelines, and modern development workflows
Strong written and verbal English communication skills

Nice To Haves

Hands-on experience with LLM-powered applications, orchestration frameworks (e.g., LangChain, LangGraph), RAG architectures, vector databases, or prompt engineering techniques
Background in applied machine learning, statistical modeling, or predictive analytics using modern frameworks (e.g., PyTorch, TensorFlow, Hugging Face, scikit-learn)
Experience with the ML lifecycle — experimentation, evaluation, monitoring, and deployment (MLOps)
Experience designing and maintaining large-scale data pipelines, analytical platforms, or data-intensive systems
Familiarity with containerization, orchestration, or Infrastructure as Code technologies (e.g., Docker, Kubernetes, Terraform)

Responsibilities

Partner with clients and cross-functional teams to translate business problems into AI solutions, defining scope, success metrics, and technical approach
Design, build, and deploy AI-enabled applications, services, and workflows, including LLM-powered systems, retrieval pipelines, and intelligent automation
Develop and maintain the APIs, backend services, and data pipelines that power scalable AI and analytical systems
Drive model evaluation, prompt engineering, data preparation, and system testing to ensure quality, reliability, and continuous improvement
Contribute to infrastructure and deployment workflows for AI systems in cloud environments
Strengthen engineering culture through code reviews, architectural documentation, mentoring, and evolving team best practices