Systems Integration Specialist
Tasks
- Apply DevSecOps practices to AI delivery
- Architect AI solutions across AWS Azure and GCP
- Architect LLM RAG and agentic AI solutions
- Architect secure zero trust AI systems
- Build MLOps deployment and monitoring pipelines
- Build data pipelines and model-serving infrastructure
- Conduct threat modeling for AI workloads
- Containerize AI workloads with Docker
- Define AI reference architectures
- Design AI ML and GenAI platforms
- Develop AI transformation roadmaps
- Ensure compliance and audit readiness
- Establish AI explainability and observability standards
- Implement CI CD pipelines for AI ML workloads
- Implement encryption access control and secrets management
- Implement infrastructure as code with Terraform
- Lead AI lifecycle management
- Orchestrate workloads with Kubernetes
- Perform red teaming and AI risk assessments
- Provide architectural governance and technical mentorship
Perks/Benefits
- N/A
Skills/Tech-stack
ARM | AWS | Access Control | Agentic AI | Azure | CI/CD | Cloud platform | CloudFormation | Data encryption | DevSecOps | Distributed Systems | Docker | Google Cloud | Google Cloud Platform | High Availability | Infrastructure as Code | Kubernetes | LLM | Langchain | MLOps | Model Deployment | Model Governance | Model Monitoring | Observability | PyTorch | Python | Retrieval-Augmented Generation | Secrets management | TensorFlow | Terraform | Threat modeling | Zero Trust | “as-code”
Education
N/A
Roles
AI | AI Architect | Architect | Engineer | Learning Engineer | Machine Learning Engineer
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