Senior AI/ML Engineer

About the role

This position supports the client's AI and ML engineering capability, focusing on model fine-tuning, agentic orchestration architecture, and LLM evaluation. The role involves overseeing vendor activities, designing integration frameworks, and ensuring the accuracy and performance of AI systems. The successful candidate will contribute to the development of internal tooling, collaborate with engineering teams, and help define the long-term roadmap for insourcing AI/ML capabilities.

Responsibilities

• Support the client's AI and ML engineering capability within the TOP platform, including model fine-tuning oversight, agentic orchestration architecture, and LLM evaluation.
• Oversee vendor fine-tuning of Google Cloud Vertex AI using proprietary diagnostic data, ensuring compliance with IP protection requirements and model weight storage architecture.
• Design and build the client's Orchestration Layer, the integration framework that connects the external AI engine with other internal AI engines and platform services.
• Evaluate AI engine outputs against defined accuracy, latency, and first-time fix rate metrics; drive iterative improvement through structured feedback loops.
• Define model evaluation frameworks and acceptance criteria for AI-generated triage recommendations, ensuring accuracy before dealer-facing deployment.
• Build internal tooling for model monitoring, drift detection, and retraining triggers within a GCP environment.
• Collaborate with the data engineering team to define data preparation and feature engineering requirements that support model fine-tuning and inference quality.
• Partner with GCP Cloud Engineers to ensure model artifact storage, versioning, and access controls comply with IP and security policies.
• Contribute to the long-term insourcing roadmap by documenting model architectures, training pipelines, and prompt frameworks.
• Represent AI and ML engineering in architecture reviews and vendor technical discussions.

Requirements

Education: Bachelor's Degree

Experience: 5 or more years of professional experience in machine learning engineering, AI systems development, or applied AI research.

Additional requirements include hands-on experience fine-tuning LLMs in a cloud environment (preferably Google Cloud Vertex AI), demonstrated experience building agentic AI systems using frameworks like LangChain or Google Agent Builder, and experience designing and evaluating LLM outputs for