AI Data Engineer

INVID•San Juan, PR

1d•Hybrid

About The Position

INVID is seeking an AI Software Data Engineer with software development experience and hands-on expertise building, integrating, and deploying AI-powered applications in production. The role calls for strong frontend and backend engineering skills, including practical experience integrating artificial intelligence, generative AI, and large language models (LLMs) into scalable, secure, and reliable software solutions. The engineer will develop end-to-end applications, design APIs and system integrations, implement AI-powered features, and ensure solutions meet production standards for performance, security, maintainability, and reliability. The role involves translating business requirements into technical solutions, owning projects throughout the software development lifecycle, and applying AI technologies to deliver measurable business value. INVID describes its culture as collaborative, flexible, and grounded in trust, respect, autonomy, and accountability. Its hybrid work model supports working both in the office and remotely. The company also highlights professional development opportunities, career pathways, industry events, and learning experiences.

Requirements

At least 5 years of professional experience in software engineering or full-stack application development.
A bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related field is preferred; equivalent professional experience may be considered.
Demonstrated experience developing and maintaining production-grade software applications.
Strong proficiency in at least one modern programming language, such as Python, JavaScript/TypeScript, Java, C#, or an equivalent technology.
Hands-on experience developing frontend and backend components, including APIs, application logic, and database integrations.
Experience integrating AI services, LLM APIs, machine learning models, or generative AI capabilities into production software applications.
Practical understanding of AI application development, including prompt design, model/API integration, output validation, and error handling.
Experience working with relational or NoSQL databases and data modeling.
Familiarity with cloud platforms, application deployment, version control, and CI/CD practices.
Knowledge of software testing, debugging, code quality, application security, and performance optimization.
Ability to troubleshoot complex technical problems and work independently in a collaborative environment.
Strong communication skills and ability to explain technical concepts to technical and nontechnical stakeholders.
Fully bilingual in English and Spanish, both written and spoken.
Must be a U.S. citizen and currently reside in Puerto Rico.

Nice To Haves

Experience building production applications using OpenAI, Azure OpenAI, Anthropic, Google Gemini, AWS Bedrock, or equivalent AI platforms.
Experience implementing RAG pipelines, embeddings, vector databases, semantic search, or AI-powered knowledge retrieval.
Familiarity with AI orchestration frameworks such as Lang Chain, Lang Graph, Llama Index, or Semantic Kernel.
Experience developing AI agents, tool-calling workflows, or multi-step automation.
Familiarity with Docker, Kubernetes, infrastructure as code, or cloud-native application architecture.
Experience with automated AI evaluation, model output monitoring, guardrails, and human-in-the-loop workflows.
Experience integrating enterprise applications, authentication services, and third-party APIs.

Responsibilities

Design, develop, test, deploy, and maintain full-stack web applications, including user interfaces, backend services, APIs, databases, and system integrations.
Build and integrate AI-powered solutions using LLMs, AI APIs, automation tools, semantic search, and intelligent workflow technologies to support business objectives.
Collaborate on software architecture, technical design, code reviews, and engineering best practices to deliver scalable, secure, and maintainable solutions.
Deploy, monitor, troubleshoot, and optimize applications in production environments using cloud technologies, CI/CD pipelines, and observability tools.
Partner with stakeholders and cross-functional teams to translate business requirements into technical solutions, manage delivery priorities, and support ongoing application enhancements.