Software Engineer, ML Platform

Jobtailor; California (MO); On-site; USD 140,000 - 200,000; Full time

Job summary

Jobtailor seeks a seasoned software engineer to help build and scale the ML/AI Platform within our product ecosystem. You will design, develop, and operate ML lifecycle infrastructure, model deployment, and observability tooling.

You will collaborate with ML/AI builders to define requirements and SLAs for API-enabled services, while advancing automated pipelines and CI/CD for ML models across cloud platforms.

Qualifications

• 5+ years of software engineering experience.
• Experience in designing and developing ML lifecycle infrastructure.
• Proficiency in Python, Ruby, or Java.
• Experience with feature stores, model development, deployment, and observability tools.

Responsibilities

• Build core ML/AI platform components including MLOps solutions with automated pipelines and standardized processes to build, deploy, run, monitor, debug, and retrain ML/AI models.
• Develop, maintain, and enhance frameworks for ML model development and deployment.
• Collaborate with ML/AI teams to determine requirements and SLAs for API-enabled services.
• Develop and maintain infrastructure supporting ML services.
• Develop new deployment patterns for ML models with CI/CD pipelines and automated testing.
• Apply AI tools in the engineering workflow and bring an AI-native lens to decisions.

Skills

MLOps Solutions

ML Lifecycle Infrastructure

CI/CD Pipelines

Python Programming

Cloud Platform Experience

Tools

AWS

Ruby

Java

Python

Job description

Requirements

• At least 5+ years of software engineering experience
• Proficiency in Python, Ruby, or Java
• Experience designing and developing machine learning lifecycle infrastructure and platform services
• Experience with feature stores, model development, deployment, and observability tools and solutions
• Experience with at least one major cloud platform; AWS preferred but not required
• Curiosity and experimentation with emerging AI frameworks
• Comfort with AI-assisted development tools
• Ability to stay current with emerging software development approaches

• Build core components of the ML and AI Platform technical roadmap, including MLOps solutions with automated pipelines and standardized processes to build, deploy, run, monitor, debug, and retrain ML and AI models
• Develop, maintain, and enhance frameworks for machine learning model development and deployment
• Collaborate with ML/AI builders and application owners to determine business requirements and SLAs for API-enabled services
• Develop, maintain, and enhance infrastructure supporting machine learning services
• Develop new deployment patterns for machine learning models with CI/CD pipelines and automated testing
• Apply AI tools in the engineering workflow and bring an AI-native lens to engineering and product decisions
• Adopt best practices for using AI technologies across technical development

Core Competencies

Demonstrates expertise in building and enhancing machine learning and AI platforms, focusing on MLOps solutions, automated pipelines, and infrastructure development. Proficient in Python, Ruby, or Java, with a strong understanding of cloud platforms and AI tools to drive innovative engineering practices.

Highest-signal resume keywords

• MLOps Solutions
• Machine Learning Lifecycle Infrastructure
• CI/CD Pipelines
• Python Programming
• Cloud Platform Experience

ATS Optimization Keywords

Hard Skills

• Machine Learning Model Development
• Automated Testing
• API Development
• Feature Stores
• Model Deployment
• Observability Tools
• Infrastructure Development
• AI Tools Application
• Software Engineering
• Emerging AI Frameworks

Soft Skills

• Curiosity
• Experimentation

Industry Keywords

• MLOps
• Automated Pipelines
• Standardized Processes
• AI-Native Engineering
• Business Requirements
• Service Level Agreements

Tools & Technologies

• AWS
• Ruby
• Java
• Python