Senior Gen AI Engineer (LLMs, RAG)
Tasks
- Architect and develop LLM solutions
- Build RAG pipelines
- Build analytics pipelines
- Build generative AI microservices
- Build knowledge bases
- Collaborate with cross-functional teams
- Create architecture diagrams
- Define problem statements and data requirements
- Deploy production AI services
- Design AI agents
- Develop model evaluation frameworks
- Document solution architecture
- Ensure enterprise security compliance
- Evaluate language models
- Fine-tune language models
- Implement AI safety guardrails
- Implement embeddings
- Implement monitoring feedback loops
- Integrate AI services with enterprise systems
- Manage knowledge base ingestion and refresh
- Mentor junior engineers
- Perform prompt engineering
- Prepare training datasets
- Present model performance insights
- Support change management
- Track tasks in JIRA
Perks/Benefits
Skills/Tech-stack
AI Agents | AI Safety | Anthropic | Azure OpenAI | Cloud Native | Cloud-native Architectures | Data Modeling | Data Pipelines | Distributed data | Embeddings | Fine Tuning | Language Models | Large Language Models | Llama | Microservices | Microsoft Azure | Model Evaluation | Model Fine-tuning | Monitoring | NoSQL | OpenAI | Operational Controls | Power BI | Prompt engineering | Python | RAG | Relational databases | Retrieval-Augmented Generation | SQL | Tableau | Vector Databases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI Engineer | Engineer | Gen AI Engineer | Senior Gen AI Engineer
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