Distributed Cloud | AI/ML Engineer
Tasks
- Build AI monitoring for output quality and hallucination detection
- Build API services with Docker
- Build vector databases pipelines
- Deploy AI applications on cloud platforms
- Deploy machine learning systems
- Design end-to-end AI pipelines
- Develop Python code for AI applications
- Develop agentic workflows
- Explore prompt engineering and fine tuning techniques
- Implement LLM Ops practices
- Integrate large language models into production
- Optimize context retrieval for retrieval augmented generation
- Track model performance
- Transition AI prototypes into scalable production systems
Perks/Benefits
- N/A
Skills/Tech-stack
API Development | AWS Bedrock | Arize Phoenix | AutoGPT | Azure AI | Chroma | Docker | FastAPI | Fine Tuning | Flask | Google Vertex | Google Vertex AI | LLMOps | Langchain | Language Models | Large Language Models | Llamaindex | Pinecone | Prompt engineering | PyTorch | Python | Ragas | Retrieval-Augmented Generation | SQL | TensorFlow | Vector Databases | Vertex AI | Weaviate
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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