Senior Machine Learning Ops Engineer

Summary

Join Nearsure's close-knit LATAM remote team and enjoy a supportive work environment with competitive USD salaries, 100% remote work flexibility, paid time off, national holidays, sick leave, a refundable annual credit, team-building activities, and a birthday day off. As a Senior Machine Learning Ops Engineer, you will design and build large-scale architectures, workflows, tools, and automation for processing data and applying machine learning engineering to solve global business challenges. You will collaborate with data engineers and data scientists, work on cloud solutions, understand the software development life cycle, and communicate effectively with other teams. Nearsure values autonomy, open communication, and respect for diversity. The People Care team provides support, and the Accounts Management team ensures smooth client relationships.

Requirements

• Bachelor's Degree in Computer Science, Engineering, or a related field
• 5+ Years of experience implementing and deploying machine learning solutions
• 5+ Years of advanced Python programming experience
• 3+ Years of experience working with SQL and Spark
• 3+ Years of experience working in cloud environments (AWS or GCP)
• Knowledge and hands-on experience with containerization tools (e.g., Docker) and orchestration tools (e.g., Kubernetes)
• Experience with version control systems, preferably Git
• Experience with workflow orchestration tools like Airflow
• Knowledge of data architectures and systems integration
• Ability to troubleshoot and solve complex software system issues
• Experience automating machine learning lifecycles and managing end-to-end ML solutions
• Experience with scripting in Bash or similar shell environments
• Understanding of software development life cycle (SDLC) and ability to integrate ML solutions with other technical teams
• Strong communication skills with the ability to teach and explain technical solutions to different stakeholders
• Advanced English Level is required for this role as you will work with US clients. Effective communication in English is essential to deliver the best solutions to our clients and expand your horizons

Responsibilities

• Architect and develop end-to-end machine learning solutions
• Manage and automate the machine learning lifecycle
• Collaborate with data engineers and data scientists to create highly scalable solutions
• Work on cloud solutions, evaluating the performance and cost of potential architectures
• Understand the software development life cycle to collaborate and integrate solutions with other technical areas
• Interact with other teams to understand business challenges and propose solutions
• Communicate and teach on how to use the developments

Preferred Qualifications

• Familiarity with FastAPI or similar web frameworks for serving ML models
• Experience with AWS SageMaker Studio and infrastructure-as-code tools such as CloudFormation

Benefits

• Competitive USD salary
• 100% remote work
• Paid time off
• National Holidays celebrated
• Sick leave
• Refundable Annual Credit
• Team-building activities
• Birthday day off