AI Engineer / AI Architect
Tasks
- Build scalable training and deployment workflows
- Communicate technical strategies effectively
- Define evaluation and rollout strategies
- Design AI pipelines and orchestration frameworks
- Design AI system architecture
- Design data ingestion and model serving strategies
- Design modular AI platforms
- Ensure AI system reliability and performance
- Establish MLOps best practices
- Establish technical documentation standards
- Guide teams in tradeoff decisions
- Implement ML systems and inference pipelines
- Lead AI system development
- Mentor engineering teams
- Operationalize prototypes into production
- Optimize distributed inference systems
- Partner with stakeholders on AI roadmaps
- Translate AI opportunities into technical solutions
Perks/Benefits
- Advanced AWS partnership
- Advanced engineering resources
- Early cloud capability access
- Specialized training
Skills/Tech-stack
AI Security | AI infrastructure | AWS | Agent systems | Automation | Automation workflows | Cloud Platforms | Code review | Communication | Cost efficiency | Data Architectures | Distributed Systems | GPU cost efficiency | Generative AI | Infrastructure as Code | LLM Systems | Lifecycle Management | ML pipelines | MLOps | Mentoring | Model Lifecycle | Model Lifecycle Management | Multi-Agent | Multi-Agent Systems | Problem Solving | Python | Retrieval-Augmented Generation | Scalable Data Architectures | System design | Technical leadership | “as-code”
Education
Roles
AI Architect | AI Engineer | AI Engineer / AI Architect | Architect
Regions
Countries
States
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