Senior AI/ML Engineer

Senior AI/ML Engineer 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. Key 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. Required Qualifications 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 production systems. A strong understanding of MLOps practices on GCP is necessary, as is experience working in regulated or IP-sensitive environments. Technical Skills: Technical Communication (2–5 years): Translating complex technical concepts for both technical and non-technical audiences. Google Cloud Platform (2–5 years): Hands-on experience with services such as Vertex AI, BigQuery, GCS, Dataflow, or Cloud Composer. TensorFlow (2–5 years): Building, training, and evaluating models, including versioning and production deployment. Data Governance (2–4 years): Applying principles of data lineage, access controls, and compliance standards. Machine Learning (3–5 years): Applied experience including feature engineering, model selection, training, and deployment with real-world data. Python (3–5 years): Writing production-quality Python for data engineering and ML pipelines using libraries like Pandas, NumPy, and scikit-learn. Artificial Intelligence & Expert Systems (3–5 years): Experience designing or working with AI systems, including LLMs and intelligent automation. Preferred Qualifications Experience in automotive diagnostics, vehicle telematics, or connected vehicle platforms. Familiarity with Diagnostic Trouble Code (DTC) data, Over-the-Air (OTA) update systems, or repair order (RO) data structures. Experience with multi-agent AI systems and tool-use patterns in production. Google Cloud Professional Machine Learning Engineer certification. Telematics (1–3 years): Exposure to vehicle data collection, event streaming, or connected vehicle platforms. Apex Systems