Remote Ai/Ml Engineer For Production Systems

Fullstack; Municipio de Rincón de los Sauces; Remote; ARS 2,500,000 - 4,200,000; Full time

Job summary

Coderio is seeking a Senior Machine Learning Ops Engineer to own the architecture, development, and automation of end‑to‑end ML solutions. You will bridge data science and software engineering, evaluate cloud architectures, and deploy scalable models into production.

You will lead ML lifecycle health, optimize cloud cost/performance, and drive high‑quality, scalable results that impact our clients globally.

Qualifications

• Bachelor’s degree in software engineering/computer science or proven equivalent experience in data engineering or ML roles.
• Hands-on experience deploying ML solutions in production.

Responsibilities

• Architect and develop robust, end-to-end machine learning solutions.
• Manage and automate the complete ML lifecycle from development to production.
• Collaborate with data engineers and data scientists to build scalable solutions and integrate with the SDLC.
• Evaluate cloud solutions and architectures for performance and cost-efficiency.
• Interface with teams and stakeholders to align technical work with business goals.
• Mentor teams and evangelize best practices for ML ops and deployment.

Skills

ML deployment

Education

Bachelor’s degree in software engineering/computer science

Tools

Python

SQL

Spark

Job description

Coderio designs and delivers scalable digital solutions for global companies. We combine strong technical expertise with a product mindset to lead complex software initiatives end-to-end. We work with international clients, value autonomy and clear communication, and build long‑term partnerships through technical excellence. More information:

This is a high‑impact technical role. We are looking for a Senior Machine Learning Ops Engineer to join our team. You will own the architecture, development, and automation of end‑to‑end machine learning solutions. Operating within an established engineering team, you will be a key player in bridging the gap between data science and software engineering, evaluating cloud solutions, and deploying scalable machine learning models into production.

This is a hands‑on execution role. You are responsible for the technical health of the ML lifecycle, the performance and cost‑efficiency of the cloud architecture, and delivering high‑quality, scalable solutions that drive business value.

What To Expect In This Role (Responsibilities)

• ML Architecture & Development: Architect and develop robust, end‑to‑end machine learning solutions.
• MLOps & Automation: Manage and automate the complete machine learning lifecycle, ensuring seamless transitions from development to production.
• Cross-Functional Collaboration: Collaborate closely with data engineers and data scientists to create highly scalable solutions and integrate them with other technical areas across the software development life cycle.
• Cloud & Architecture Strategy: Work on cloud solutions, actively evaluating the performance and cost‑efficiency of potential architectures.
• Business Alignment: Interact with other teams and stakeholders to deeply understand business challenges and propose effective technical solutions.
• Mentorship & Advocacy: Communicate technical concepts clearly and teach teams how to properly use and adopt the new developments.

Requirements

• Educational Foundation: Bachelor’s degree in software engineering/computer science, or proven equivalent experience in data engineering or machine learning roles.
• ML Deployment Mastery: Proven hands‑on experience implementing and deploying machine learning solutions in production environments.
• Core Stack Expertise: Advanced knowledge of Python, SQL, and Spark.
• Tooling & Infrastructure: Strong knowledge in (or similar to) Docker, Git, Bash Scripting, FastAPI, Sagemaker Studio, Airflow, and CloudFormation.