Software Engineer III - AI/ML Platform Engineer

Job summary

The Software Engineer III role focuses on building and enhancing platform capabilities for the machine learning lifecycle, including model operations and governance tooling. Key responsibilities include developing cloud-native microservices, creating large language model-powered capabilities, and collaborating with cross-functional teams to deliver reliable platform services.

Qualifications

• Formal training or certification on software engineering concepts and 3+ years applied experience
• Experience with modern architecture patterns such as microservices, reactive architectures, event-driven architectures
• Experience developing and deploying large language model-powered solutions using prompt design, agent workflows, tool integration, and retrieval-augmented generation
• Programming experience in at least two modern languages or frameworks such as Python, Java, JavaScript, React, Node.js
• Experience building and consuming RESTful application programming interfaces and tuning performance in large-scale applications
• Experience with cloud platforms and containerization or orchestration such as Docker and Kubernetes
• Experience with relational and non-relational databases such as PostgreSQL, MongoDB, Redis, Elasticsearch, Cassandra
• Experience with engineering practices including refactoring, design patterns, test-driven development, continuous integration and delivery, and application security
• Hands-on experience using AI-assisted software development tools, with the ability to validate and refine outputs for correctness, performance, and security

Responsibilities

• Build and enhance platform capabilities for model registries, feature registries, promotion policies, and governance tooling
• Design and deliver services for data preparation, annotation workflows, lineage, and auditability
• Develop cloud-native microservices and application programming interfaces to support scalable model delivery and operations
• Create large language model-powered capabilities to deliver grounded, reliable outcomes
• Produce architecture and design artifacts ensuring implementations meet performance, resiliency, and security constraints
• Use AI-assisted development tools to improve quality and speed, validating outputs via reviews, testing, and secure coding practices
• Analyze telemetry and user feedback; build reporting and metrics to drive continuous improvement
• Identify hidden issues and patterns in systems and data; improve code health, observability, and platform architecture

Skills

Relocation