Offer summary

Qualifications:

3+ years of experience in ML or ML Ops in a production environment., Proficiency in Python, Terraform, and data warehousing tools like SQL and BigQuery., Experience with cloud platforms such as GCP, AWS, or Azure, and container-based deployments like Docker and Kubernetes., Strong teamwork and communication skills, with an open-minded approach to collaboration..

Key responsibilities:

Design and implement improvements to the ML platform for efficient training and maintenance of ML models.

Collaborate with ML engineers, product managers, and software engineers to understand use cases and pipelines.

Set standards for running workflows in machine learning and implement CI/CD for models and data pipelines.

Expand the data warehouse with new data sources and features, including dashboards.

Job description

Job Description

Machine Learning Operations Engineer

Reports to: Head of AI
Location: Latin America
Hours: Full-time
Compensation: $45,000 - $75,000 Base Salary + Equity

About ReflexAI

ReflexAI brings the best in machine learning and natural language processing to mission-driven, people-centric organizations via innovative tools that transform how they train, develop, and empower their frontline teams. The AI / ML team at ReflexAI is involved in multiple large-scale projects, including ReflexAI’s highly visible development of a tool that trains veterans to support each other through mental health challenges.

About this role

As a Machine Learning Operations Engineer at ReflexAI, you will be involved in multiple large-scale projects across building, iterating, and maintenance of the ML platform. The purpose of the ML platform is to accelerate development, deployment, and maintenance of ML models. To be successful, you should have significant experience with ML system design, cloud platforms, databases, and logging/monitoring/alerting systems. You will also collaborate with software engineering, product, and business team members. You should be excited about being a part of a fast-paced startup that makes a real impact across important industries including crisis response, public safety, healthcare, and more.

What you’ll do

Design, plan, and implement changes and improvements to our ML platform for efficient, large-scale training and maintenance of ML models and multi-platform support
Work cross-functionally with ML engineers, product managers, and software engineers to understand the various use cases and pipelines needed
Set standards and procedures on how to run workflows for machine learning
Implement continuous integration and continuous deployment (CI/CD) for machine learning models and data pipelines
Expand the data warehouse with new data sources, access features, and dashboards

Requirements for a great fit

Significant experience (typically 3+ years) of ML or ML Ops experience in a production environment
Significant experience (typically 3+ years) building end-to-end scalable ML infrastructure and data pipelines with on-premise or cloud platforms including Google Cloud Platform (GCP), Amazon Web Services (AWS) or Azure
Significant experience (typically 3+ years) with container-based deployments (e.g., Docker, Kubernetes)
Experience with developing machine learning models and LLM applications is a plus
Experience in building ML systems with robust logging, monitoring, and alerting systems.
Experience in building load testing systems to support scalable ML Ops.
Proficiency in Python, Terraform, and data warehousing languages and tools (SQL, dbt, BigQuery, Fivetran, etc.)
Strong teamwork skills including communication and collaboration with both technical and non-technical team members
Open mindedness as demonstrated by ability to consider other perspectives and feedback, ability to engage in discussions with professionalism and empathy, and a strong desire to learn

Benefits

Generous retirement, equity, healthcare, and PTO policies
Flexibility to work remotely from anywhere in Latin Americs
ReflexAI is an equal opportunity employer. We are committed to equal employment opportunity regardless of race/ethnicity, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, military status, neurodiversity, or any other federal, state or local protected class in the United States

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