AI Engineer (LLMs & Generative AI)
Tasks
- Benchmark models
- Build RAG pipelines
- Build evaluation frameworks
- Build fallback routing strategies
- Chunk and embed data
- Collaborate with cross-functional teams
- Conduct A/B testing
- Create vector indexes
- Defend against prompt injection
- Deploy generative AI models
- Design LLM applications
- Develop evaluation metrics
- Document AI architectures
- Ensure scalability and observability
- Filter and moderate content
- Implement AI guardrails
- Implement CI/CD pipelines
- Implement model orchestration
- Ingest and preprocess data
- Integrate AI into enterprise applications
- Mitigate hallucinations
- Perform context retrieval and ranking
- Support cost optimization
- Test and monitor AI services
- Validate and verify model outputs
Perks/Benefits
Skills/Tech-stack
A/B | A/B Testing | AI Safety | AI guardrails | AWS | Access Controls | Anthropic API | Azure | Azure OpenAI | B testing | CI/CD | Chunking | Content Moderation | Context retrieval | Data Ingestion | Data Preprocessing | Databricks | Docker | Embeddings | Faiss | GCP | Generative AI | Hallucination mitigation | Hugging Face | Indexing | Injection Defense | Kubernetes | Langchain | Language Models | Large Language Models | Llamaindex | MLOps | Model Evaluation | Monitoring | OpenAI API | Pinecone | Prompt engineering | Prompt injection | Prompt injection defense | Python | RAG | Retrieval-Augmented Generation | Semantic Kernel | Spark | Vector Databases | Vector Search | Weaviate
Education
N/A
Roles
Regions
Countries
States
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