ML Platform Engineer

Truist•Raleigh, NC

21h•Onsite

About The Position

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.

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.

Nice To Haves

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 software components and services that address defined business and user requirements within an assigned technical domain.
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.
Collaborates with cross-functional technical teams to execute planned technical initiatives and deliver solutions on schedule.
Troubleshoots, debugs, and resolves software defects and production issues within 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.
Leverages existing frameworks, tools, and technologies to build and enhance applications across relevant technology domains.