Senior Applied AI Engineer
Tasks
- Architect end to end AI ML systems for quality engineering workflows
- Build model evaluation frameworks for accuracy robustness and safety
- Build production AI systems on Google Cloud Vertex AI including training tuning pipelines vector search and online endpoints
- Collaborate with DevOps SRE and Infosec to embed AI into the SDLC
- Create model governance and safety standards
- Deploy RAG pipelines with retrieval optimization and hallucination mitigation
- Detect quality risks using logs traces telemetry and user signals
- Develop LLM tools for test case generation synthetic data creation telemetry summarization and automated triage
- Ensure training data quality governance privacy and compliance
- Integrate AI into CI CD pipelines for risk based testing and quality gates
- Integrate Vertex AI with BigQuery Cloud Run GKE and PubSub for deployment
- Lead LLM system design and MLOps monitoring strategies
- Mentor engineers on applied ML modern AI prompt engineering and RAG vs fine tuning
- Perform LLM fine tuning and evaluate when to use RAG vs fine tuning
Perks/Benefits
- 401k plan
- Disability benefits
- Life insurance coverage
- Medical/Dental/Vision insurance
- Paid time off
- Tuition assistance program
Skills/Tech-stack
AI Pipelines | AI vector search | Amazon SageMaker | Appium | BigQuery | CI/CD | Cloud Run | Databricks | Fine Tuning | GKE | Google Cloud | Google Cloud Vertex | Google Cloud Vertex AI | Hallucination mitigation | Hybrid retrieval | Instruction Tuning | JUnit | Java | Kubeflow | Kubernetes | LLM Fine-tuning | Language Models | Large Language Models | LoRA | MLOps | MLflow | Model Evaluation | Monitoring | Observability | Online Endpoints | OpenTelemetry | Playwright | Prompt engineering | PubSub | PyTorch | Pytest | Python | QLoRA | RAG | Selenium | TensorFlow | Terraform | TestNG | Vector Databases | Vector Search | Vertex AI | Vertex AI Vector Search | Vertex AI pipelines
Education
N/A
Regions
Countries
States
Cities
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