ML Engineer
Tasks
- Build AI/ML workflows
- Build LLM evaluation prompt management and safety guardrails
- Build Python AI apps on GKE
- Create GenAI templates and reusable tooling
- Deploy RAG and agent based application patterns
- Develop AI ML pipelines and models
- Implement GitOps deployment workflows
- Implement logging alerting and SLOs for ML workloads
- Monitor model drift and quality
- Optimize model scalability
- Support incident response and root cause analysis
- Support production model monitoring debugging and retraining
- Troubleshoot AI ML and GenAI pipelines
Perks/Benefits
Skills/Tech-stack
Airflow | Alerting | Apache Kafka | Argo CD | BigQuery | Codefresh | Data Proc | Dataflow | Deep learning | Docker | Drift Detection | Embeddings | GKE | Git | GitHub Actions | GitOps | Kubernetes | LLM Evaluation | Langchain | Langgraph | Logging | Machine Learning | Model Drift | Model Drift Detection | Model Monitoring | New Relic | Observability | PagerDuty | Prompt engineering | PyTorch | Python | RAG | Retrieval-Augmented Generation | SLO | SRE | Splunk | TensorFlow | Vector Database | Vertex AI
Education
Roles
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