Senior Engineer, Aspiring AI Builder

Our client is a startup growing fast enough that machine learning is becoming core to the product's direction. They're ready to move ML out of prototypes and into production, but they don't yet have any in-house ML expertise.

This role is built for a strong, self-directed software engineer who's been tinkering 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 the early calls, and building the foundation others will eventually work on top of. Your senior engineering background is what 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. If you're the kind of engineer who learns by building, reads the docs and the papers, and gets a kick out of being handed an open problem, this is a rare chance to own AI from the ground up., * Completed background checks will be required before the start date if you are selected as a winning candidate.

• As a winning candidate, you will be required to disclose your engagement with DevEngine as a primary client on your professional LinkedIn profile.

While we strive to respond to all applicants, please understand that due to the high volume of applications we receive, providing individual feedback or responses to every candidate may not be feasible. Rest assured that your application will be carefully reviewed and considered. We appreciate your understanding and interest in joining our team.

Do you have experience in Tooling?, This is a long-term contract opportunity without an end date. The engagement is fully remote. Candidates must be located in Latin America. You'll need strong English communication skills (B2+ or higher) for this role. Mandatory Requirements

• 5+ years of professional software engineering experience, with a track record of shipping and maintaining production systems.
• Strong Python proficiency, it's the primary language for this work, and you're comfortable writing clean, production-grade code in it.
• A demonstrated ability to teach yourself hard things: you've picked up new domains, tools, or stacks on your own and gotten them to production. This is the single most important quality for this role.
• Hands-on tinkering with AI/ML: side projects, experiments, coursework, or professional exposure. You don't need years of it, but you've gone past reading about it and actually built things.
• 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: you can explain technical tradeoffs clearly to both technical and non-technical stakeholders, which matters more when you're the only ML voice in the room.

Nice-to-Have

• Hands-on experience with a major ML framework (PyTorch, TensorFlow, or similar) and the core data ecosystem (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.