Lead Software Platform Engineer, MLOps
Tasks
- Build evaluation and quality infrastructure
- Build model and prompt lifecycle
- Design real time and batch inference substrate
- Ensure reproducibility and data lineage
- Establish observability with distributed tracing
- Implement AI platform security for multi tenant
- Implement infrastructure-as-code and deployment automation
- Integrate RAG and agent tool calling
- Lead design reviews and write technical documentation
- Own AI ML platform architecture
- Own production readiness and incident response
- Set technical direction for emerging AI infrastructure
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | API Design | AWS Bedrock | AWS CDK | Agent runtimes | Alerting | Audit trails | Authorization | B testing | Batching | CI/CD | Caching | Capacity Planning | Cloud Native | CloudFormation | Concurrency Control | Databricks MLFlow | Distributed Systems | Distributed tracing | Docker | Drift Detection | Embeddings | Function Calling | GPU capacity | GPU capacity planning | Hallucination detection | Infrastructure as Code | Injection Defense | Logging | MCP | MLOps | Model Registry | Model Serving | Model versioning | Monitoring | Multi-tenancy | Offline evaluation | Online evaluation | OpenAPI | PII | Phi | Prompt engineering | Prompt injection | Prompt injection defense | Prompt versioning | Python | RAG | REST | Regression testing | Retrieval-Augmented Generation | SLA | SLI | SLO | Shadow Deployment | Tool Abuse | Tool Abuse Defense | Tool-Calling | TypeScript | “as-code”
Education
N/A
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