Senior AI Platform Engineer
Tasks
- Architect training pipelines
- Build agent runtime infrastructure
- Create internal enablement resources
- Deploy machine learning models
- Evaluate MLOps tooling
- Implement AI/ML governance
- Implement agent memory systems
- Implement monitoring for drift
- Integrate MLOps with SDLC
- Maintain documentation
- Maintain training pipelines
- Mentor junior engineers
- Monitor model quality reliability scalability
- Operationalize ML models at scale
- Provide observability for ML systems
- Secure model development
- Set up secure gateways for agents
- Validate and maintain models
- Version and test models
Perks/Benefits
- Cell phone stipend
- Conference Benefit
- Continuous learning opportunities
- Fitness stipend
- Full remote flexibility
- Health benefits
- Parental leave
- Unlimited PTO
Skills/Tech-stack
AI/ML | AI/ML Governance | AWS | Agent Orchestration | Amazon Bedrock | Bedrock AgentCore | CI/CD | Concept drift | Concept drift monitoring | Containers | Data Drift | Data Drift Detection | Docker | Drift Detection | Drift monitoring | EKS | Infrastructure as Code | LLM | ML Governance | MLOps | Memory systems | Model versioning | Monitoring | Observability | PyTorch | Python | RAG | SDLC | SageMaker | Scikit-learn | Secure Gateways | Security | Spark | Step Functions | TensorFlow | Testing | Tool-Calling | Validation | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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