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 global 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 OpenAI, 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. #J-18808-Ljbffr