Get AI-powered advice on this job and more exclusive features. Employment Type:
Contract-to-Hire (6 months with intent to convert to FTE) Location:
Remote, Mexico Overview
We are seeking a
Data Science / AI Software Engineer
to join a integral AI and data engineering team focused on building enterprise-grade generative and agentic AI applications. The ideal candidate brings a
software engineering background with strong data experience —comfortable integrating large-language-model APIs and creating composable AI architectures rather than developing models from scratch. Key Responsibilities
Design, develop, and deploy
AI-driven applications
leveraging generative and agentic AI capabilities. Build scalable
backend and frontend components
that integrate with LLM APIs and enterprise data sources. Develop and optimize data workflows using
SQL
and
Python. Integrate AI solutions with data platforms and systems such as
Snowflake, AWS, or GCP. Utilize
Docker
and modern development practices to create modular, composable solutions. Automate and orchestrate data and AI pipelines to streamline business processes.
Apply best practices in
software engineering —version control, modularity, and secure integration. Collaborate with cross-functional teams including data engineering, data science, and strategy to implement practical AI-driven automation use cases. Qualifications
Bachelor’s degree in
Computer Science, Software Engineering, or related field
(or equivalent experience). 4–7 years of hands‑on
software engineering experience. Proficiency in
Python
and
SQL
for backend and data operations. Experience building AI applications on
cloud data platforms
(e.g., Snowflake Cortex, AWS Bedrock, Azure Open AI, or GCP Vertex AI). Knowledge of
Docker , composable architecture, and API integration. Familiarity with
generative AI / agentic AI
principles and LLM APIs. Exposure to
machine learning techniques
(classification, regression, clustering) a plus. Excellent problem‑solving and communication skills. Spanish or bilingual capability preferred but not required.