ML Platform Engineer

Jobtailor; North Carolina; On-site; USD 100,000 - 180,000; Full time

Job summary

Jobtailor is hiring for a senior software/ML platform engineer to design, develop, test, and maintain scalable software and agentic AI platform solutions. You will build and support multi-tenant ML environments, applying established engineering and MLOps practices to deliver reliable and secure solutions.

The role involves collaborating with cross‑functional teams, implementing secure coding practices, and contributing throughout the SDLC, including deployment and support.

Qualifications

• Bachelor’s degree and 3 years of experience or equivalent education and software engineering training or experience.
• In‑depth knowledge of information systems with the ability to identify, apply, and implement IT best practices.
• Understanding of key business processes and competitive strategies related to the IT function.
• Bachelor’s degree in computer science, computer engineering, or related field with eight years of experience, or equivalent combination of education and work experience.
• Strong foundation in software engineering, including data structures, algorithms, system design, and enterprise application development, with experience scaling solutions from concept to production.
• Experience designing and developing multi‑tenant platforms with capabilities for tenant isolation, governance, scalability, and support for multiple teams and use cases.
• Hands‑on experience with ML platform components, including feature stores, model training pipelines, model registry, inference services, and monitoring frameworks.
• Proven experience with MLOps and platform engineering practices, including CI/CD for ML, automated deployment, lifecycle management, and reproducibility.
• Experience building scalable, secure, and cost‑efficient cloud‑based platforms (e.g., Azure, AWS), including architecture patterns for multi‑tenant deployments.
• Experience designing and implementing agentic AI platforms or frameworks, including agent orchestration, multi‑agent workflows, tool integration (APIs, retrieval systems), and memory/reasoning patterns using LLMs is a plus.
• Knowledge of governance, security, and responsible AI practices, including data/model governance, regulatory considerations (especially in financial services), and controls for monitoring, auditability, and safe AI adoption is a plus.

Responsibilities

• Designs, develops, tests, and maintains scalable software, machine learning, and agentic AI platform solutions within a defined technical domain.
• Builds and supports multi‑tenant ML and agentic platforms using established engineering and MLOps practices.
• Delivers reliable, secure, and high‑quality solutions while collaborating with cross‑functional teams to execute well‑scoped initiatives and enhance platform capabilities.
• Implements well‑scoped features and enhancements using established coding standards, architectural patterns, and development best practices.
• Contributes to the reliability, scalability, and performance of applications by writing high‑quality, maintainable code and participating in peer code reviews.
• Troubleshoots, debugs, and resolves software defects and production issues within the area of responsibility, applying root‑cause analysis as needed.
• Participates in the full software development lifecycle, including requirements refinement, design discussions, development, testing, deployment, and support.
• Applies secure coding practices, testing strategies, and documentation standards to ensure software quality and compliance with team guidelines.

Skills

Software engineering

MLOps

Cloud platforms

CI/CD

Multi-tenant architecture

LLMs / agentic AI

Security & compliance

Education

Bachelor’s degree in computer science, computer engineering, or related field

Eight years of experience or equivalent education

Tools

Azure

AWS

CI/CD tools

Job description

Responsibilities

• Designs, develops, tests, and maintains scalable software, machine learning, and agentic AI platform solutions within a defined technical domain.
• Builds and supports multi‑tenant ML and agentic platforms using established engineering and MLOps practices.
• Delivers reliable, secure, and high‑quality solutions while collaborating with cross‑functional teams to execute well‑scoped initiatives and enhance platform capabilities.
• Implements well‑scoped features and enhancements using established coding standards, architectural patterns, and development best practices.
• Contributes to the reliability, scalability, and performance of applications by writing high‑quality, maintainable code and participating in peer code reviews.
• Troubleshoots, debugs, and resolves software defects and production issues within the area of responsibility, applying root‑cause analysis as needed.
• Participates in the full software development lifecycle, including requirements refinement, design discussions, development, testing, deployment, and support.
• Applies secure coding practices, testing strategies, and documentation standards to ensure software quality and compliance with team guidelines.

Requirements

• Bachelor’s degree and 3 years of experience or equivalent education and software engineering training or experience.
• In‑depth knowledge of information systems with the ability to identify, apply, and implement IT best practices.
• Understanding of key business processes and competitive strategies related to the IT function.
• Bachelor’s degree in computer science, computer engineering, or related field with eight years of experience, or equivalent combination of education and work experience.
• Strong foundation in software engineering, including data structures, algorithms, system design, and enterprise application development, with experience scaling solutions from concept to production.
• Experience designing and developing multi‑tenant platforms with capabilities for tenant isolation, governance, scalability, and support for multiple teams and use cases.
• Hands‑on experience with ML platform components, including feature stores, model training pipelines, model registry, inference services, and monitoring frameworks.
• Proven experience with MLOps and platform engineering practices, including CI/CD for ML, automated deployment, lifecycle management, and reproducibility.
• Experience building scalable, secure, and cost‑efficient cloud‑based platforms (e.g., Azure, AWS), including architecture patterns for multi‑tenant deployments.
• Experience designing and implementing agentic AI platforms or frameworks, including agent orchestration, multi‑agent workflows, tool integration (APIs, retrieval systems), and memory/reasoning patterns using LLMs is a plus.
• Knowledge of governance, security, and responsible AI practices, including data/model governance, regulatory considerations (especially in financial services), and controls for monitoring, auditability, and safe AI adoption is a plus.