ML Engineer
Tasks
- Build AI ML and GenAI pipelines
- Build AI powered Python apps
- Build RAG and agent based applications
- Create GenAI templates and reusable tooling
- Deploy to GKE Kubernetes using GitOps
- Develop LLM evaluation and safety guardrails
- Develop internal platform tooling
- Implement LLMOps with model gateways
- Implement logging alerting and SLOs
- Implement scalable LLM evaluation pipelines
- Implement traffic management with service mesh
- Improve platform reliability and governance
- Integrate enterprise security tooling
- Integrate monitoring for LLMs
- Monitor model drift and quality
- Orchestrate ML workflows end to end
- Support incident response and root cause analysis
Perks/Benefits
- Flexible remote work up to four weeks per year
- Generous time off
- Health & wellness coverage
- Hybrid work model
- Paid volunteer days
- Parental leave
- Retirement plan with company contributions
- Travel discounts
- Tuition support
Skills/Tech-stack
AI Pipelines | AI Workbench | Airflow | Apache Spark | ArgoCD | BigQuery | Cloud Dataflow | Composer | Dataproc | Docker | Gemini Enterprise | GitHub Actions | GitOps | Google Cloud | Google Cloud Dataflow | Google Kubernetes | Google Kubernetes Engine | Istio | Istio gateways | Kubernetes | Kubernetes Engine | LLM Evaluation | LLMOps | Langchain | Langgraph | MLOps | Machine Learning | Monitoring | New Relic | NexusIQ | Observability | PagerDuty | Prompt engineering | Python | RAG | Retrieval-Augmented Generation | Service Mesh | Splunk | StackRox | Vector Databases | Vertex AI | Vertex AI Workbench | Vertex AI pipelines | Wiz
Education
Roles
Data Scientist | Engineer | ML Engineer | MLOps Engineer | Scientist
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