Senior Engineer, Aspiring AI Builder

About the Role

Our client is a startup growing fast enough that machine learning is becoming core to the product's direction. They are ready to move ML out of prototypes and into production, but they have no in‑house ML expertise. This role is built for a strong, self‑directed software engineer who has tinkered with AI/ML and wants to make it the center of their work.

You won't be inheriting an established ML practice or a senior specialist to lean on. You'll be the one figuring things out, making early calls, and building the foundation others will eventually work on top of. Your senior engineering background makes that possible: you already know how to ship reliable production systems, and you'll apply that discipline to ML while teaching yourself the modelling side as you go.

Long‑term contract opportunity without an end date. The engagement is fully remote. Candidates must be located in Latin America. Strong English communication skills (B2+ or higher) are required.

• Build and ship reliable production ML systems from scratch.
• Define early technical direction for ML integration, make calls, and lay the foundation for future work.
• Design, build, and maintain data pipelines and backend services that move and transform data at scale.
• Deploy, monitor, and iterate ML models in production environments.
• Explain technical trade‑offs clearly to both technical and non‑technical stakeholders.

• 5+ years of professional software engineering experience, with a track record of shipping and maintaining production systems.
• Strong Python proficiency, with a focus on writing clean, production‑grade code.
• Demonstrated ability to teach yourself hard things: pick up new domains, tools, or stacks and get 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 communication skills: explain technical trade‑offs clearly to both technical and non‑technical stakeholders.

• Hands‑on experience with a major ML framework (PyTorch, Tensor Flow, or similar) and the core data ecosystem (pandas, Num Py, 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.

• Completed background checks will be required before the start date if you are selected.