AI/ML Engineer
Tasks
- Apply ML evaluation
- Apply MLOps practices
- Build data pipelines
- Build vector storage retrieval logic
- Deploy containerized workloads
- Design RAG pipelines
- Develop APIs
- Develop LLM-powered applications
- Ensure performance reliability security
- Implement embedding generation
- Implement traditional machine learning models
- Optimize LLM applications for Q A summarization classification
- Orchestrate LLM workflows
- Perform cloud integrations
- Tune regression clustering feature engineering
Perks/Benefits
- N/A
Skills/Tech-stack
API Development | AWS | Algorithms | Azure | BentoML | Clustering | Data Governance | Data Structures | Deep learning | Distributed Systems | Docker | Embeddings | Feature Engineering | Huggingface | LLM orchestration | Language Models | Language Processing | Large Language Models | MLOps | MLflow | Machine Learning | Model Evaluation | Natural Language | Natural Language Processing | Prompt engineering | PyTorch | Python | RAG Pipelines | Regression | Retrieval-Augmented Generation | Security | TensorFlow | Vector Databases
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
Regions
Countries
States
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