Key Responsibilities and Deliverables

1. Design and build AI solutions for infrastructure validation and TRR automation, including a validation assistant for engineers.

2. Develop intelligent automation that integrates with Azure DevOps (ADO) for pipelines, bug management, and workflow orchestration.

3. Create AI/Copilot systems for predictive analytics and diagnostics across hardware.

4. Build data ingestion and correlation pipelines across multi-source telemetry, logs, and firmware data to support RCA and compliance validation.

5. Implement an AI-driven log analytics platform with intelligent notifications, anomaly detection, and guided remediation workflows.

6. Deliver a bug automation engine integrated with ADO and a knowledge copilot to surface similar incidents, fixes, and best practices.

7. Develop predictive failure detection and firmware compatibility analysis to reduce downtime and improve deployment readiness.

Required Skills & Qualifications

1. LLM engineering: Prompt engineering, retrieval-augmented generation (RAG), fine-tuning/evaluation, and safety/quality best practices.

2. Log analytics, anomaly detection, and root-cause prediction models.

3. Programming: Strong Python; C#, Powershell is a plus for infrastructure integration.

4. AI/ML frameworks & cloud: Azure AI services, OpenAI; experience with PyTorch.

5. Working with large-scale logs, telemetry, building reliable data pipelines.

6. Familiarity with Azure DevOps, pipelines, and bug/incident management systems.

7. Scalable AI system design, including API-based microservices architectures.

Preferred Domain Knowledge

1. Hardware validation and infrastructure; debugging workflows and failure analysis.

2. Strong problem-solving and analytical thinking in high-scale, data-intensive systems.

3. Ability to collaborate effectively with validation engineers, and infrastructure teams.

Gen AI Engineer