AI Engineer – AI Engineering & Platforms (AI Centre of Excellence)
Tasks
- Apply Infrastructure as Code with Terraform
- Build AI services and components
- Build data grounding strategies with embeddings and vector search
- Build data pipelines and feature flows
- Collaborate with product teams for discovery and delivery
- Create prompt and system designs
- Deploy AI services using containers and orchestration
- Design AI solution architectures
- Develop LLM based workflows
- Develop reusable AI libraries APIs services and templates
- Embed security privacy and safety controls
- Implement AI platform tooling and MLOps practices
- Implement LLM evaluation and testing
- Implement access control logging and guardrails
- Implement retrieval-augmented generation
- Integrate AI providers via stable interfaces
- Integrate monitoring observability metrics and tracing
Perks/Benefits
- N/A
Skills/Tech-stack
Access Control | Automated testing | Azure | Azure OpenAI | CI/CD | DBT | Docker | Embeddings | Evaluation | Event Driven | Event-driven architecture | Feature Store | Git | Google Cloud | GraphQL | Guardrails | Infrastructure as Code | JavaScript | Kubernetes | Logging | MLOps | Metrics | Microservices | Model Registry | Node.js | Observability | Privacy | Prompt engineering | Python | REST | React | Retrieval-Augmented Generation | Security | Snowflake | TDD | Terraform | Tracing | TypeScript | Vector Search | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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