Engineering Manager, MLOps
Tasks
- Catch delivery slippage early
- Drive commitments to completion
- Evaluate technical risk and adjust plans
- Handle underperformance directly
- Manage stakeholder expectations and visibility
- Own quarterly roadmap delivery end to end
- Perform reliability latency and cost tradeoffs
- Plan epics and sequence dependencies
- Represent delivery constraints to stakeholders
- Run one on ones provide feedback growth and retention
- Run standups retros and reviews
- Set engineer expectations and track performance
- Write and review code and run proofs of concept
Perks/Benefits
- Equity or stock options
- Home office stipend
- Hybrid work system
- Meal allowance
- Opportunity to travel for training and collaboration
- Paid parental leave
- Paid sick days
- Training reimbursement
Skills/Tech-stack
AI Foundry | AWS Bedrock | Agent evaluation | Agent runtime | Argo CD | ArgoCD | Azure AI | Azure AI Foundry | Evaluation | GitOps | Google Vertex | Google Vertex AI | Helm | Infrastructure as Code | Kagent | Kubernetes | LLM Gateway | LLM-as-a-Judge | LOCAL model | Langfuse | Language Models | Large Language Models | Litellm | Local Model Serving | MLOps | Machine Learning | Model Serving | Multi-tenant | Multi-tenant SaaS | Observability | Ollama | Prompt engineering | Prompt versioning | Quotas | Rate Limiting | Regulated Environment | Security Compliance | Terraform | Tracing | Vertex AI | “as-code”
Education
N/A
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