Senior Engineer, Aspiring AI Builder (Remote, LATAM)

About the role

Our client is a fast-growing startup where machine learning is becoming core to the product. They're ready to move ML out of prototypes and into production but currently lack in-house ML expertise. This role is for a strong, self-directed software engineer with AI/ML experience who wants to make it the center of their work. You'll be the first ML hire, responsible for figuring things out, making early calls, and building the foundation for future team members. Your senior engineering background will help you ship reliable production systems while teaching yourself the modeling side. This is a long-term, fully remote contract opportunity with no end date, for candidates located in Latin America.

Responsibilities

• Own AI/ML initiatives from the ground up, establishing best practices and infrastructure.
• Design, build, and deploy production-grade ML systems and data pipelines.
• Make technical decisions independently and communicate tradeoffs to stakeholders.
• Self-educate on ML modeling while applying engineering discipline to production systems.

Requirements

• 5+ years of professional software engineering experience, with a track record of shipping and maintaining production systems.
• Strong Python proficiency for production-grade code.
• Demonstrated ability to teach yourself hard things and bring them to production.
• Hands-on tinkering with AI/ML (side projects, experiments, coursework, or professional exposure).
• Solid experience deploying and operating applications on cloud infrastructure (AWS, GCP, or Azure).
• Experience building data pipelines or backend services that move and transform data at scale.
• Strong English communication skills (B2+ or higher) to explain technical tradeoffs to diverse stakeholders.

Nice-to-Have

• Hands-on experience with major ML frameworks (PyTorch, TensorFlow) and data tools (pandas, NumPy, scikit-learn).
• Experience taking ML models into production, including deployment and monitoring.
• Familiarity with MLOps tooling (MLflow, Weights & Biases, Airflow).
• Exposure to LLMs, RAG architectures, fine-tuning, or building LLM-powered applications.
• Background in financial services, healthcare, or another regulated industry.
• Contributions to open-source ML projects or public technical work.

Note: Completed background checks and LinkedIn profile updates will be required for selected candidates. Due to high application volume, individual feedback may not be provided.