Senior ML Ops Engineer: Production ML & Cloud Automation

Fullstack; Municipio de Rincón de los Sauces; Remote; ARS 400,000 - 650,000; Full time

Job summary

Fullstack is seeking a Senior Machine Learning Ops Engineer to own architecture, development, and automation of end-to-end ML solutions. You will bridge data science and software engineering, evaluating cloud options, and deploying scalable models into production.

This hands-on role emphasizes technical health of the ML lifecycle, cost-efficient cloud architectures, and delivering high-quality systems that drive business value in a global client environment.

Qualifications

• Bachelor’s degree in software engineering/computer science or proven equivalent experience in data engineering or ML roles.
• Hands-on experience implementing and deploying machine learning solutions in production environments.
• Advanced knowledge of Python, SQL, and Spark.

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.

Skills

Python

SQL

Spark

Education

Bachelor’s degree in software engineering / computer science

Job description

Fullstack is seeking a Senior Machine Learning Ops Engineer to own architecture, development, and automation of end-to-end ML solutions. You will bridge data science and software engineering, evaluating cloud options, and deploying scalable models into production.

This hands-on role emphasizes technical health of the ML lifecycle, cost-efficient cloud architectures, and delivering high-quality systems that drive business value in a global client environment.