AI Architect & RAG Data Science Engineer
Tasks
- Build search and retrieval pipelines
- Create RESTful APIs for AI services
- Design RAG solution architectures
- Develop evaluation and assurance frameworks
- Ensure provenance and access controls
- Implement DevSecOps and MLOps workflows
- Implement secure RAG and agent workflows
- Integrate structured and unstructured data
- Manage model and data versioning
- Set up monitoring and observability for AI systems
Perks/Benefits
Skills/Tech-stack
Access Management | Azure | Azure DevOps | BM25 | C# | CI/CD | Data Versioning | Docker | Embeddings | Git | GitHub Actions | GitLab | Hybrid retrieval | Identity and Access Management | Identity and access | Infrastructure as Code | JSON | Java | Kubernetes | LLM Inference | LLM guardrails | MLOps | Metadata filtering | Model versioning | Monitoring | Observability | Prompt engineering | Python | RAG evaluation | REST APIs | Reranking | SQL | Security clearance | TF-IDF | Vector Databases | Vulnerability Management | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI Architect | Architect | Data Scientist | Engineer | Learning Engineer | Machine Learning Engineer | Scientist
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