Tech_AI Engineer
Tasks
- Build LLM integrated APIs and services
- Build RAG systems
- Control costs for model deployment
- Create technical documentation and transfer knowledge
- Define AI technical architectures
- Deploy monitor and govern AI systems
- Develop LLM-based applications
- Develop, train, evaluate, and optimize machine learning models
- Ensure security privacy compliance for AI systems
- Evaluate AI trends and migrate to production
- Fine-tune and adapt LLMs
- Implement MLOps and LLMOps practices
- Integrate structured and unstructured data sources
- Optimize LLM inference
- Teach technical training for domain topics
Perks/Benefits
- Continuous learning
- Flexible working hours
- Hybrid remote work
- Social benefits
- Workplace volunteering
Skills/Tech-stack
AWS | Agile | Azure | CI/CD | Compliance | CrewAI | Deep learning | Docker | Git | Google Cloud | Kubernetes | LLMOps | Langchain | Langgraph | Language Models | Large Language Models | LoRA | MLOps | Machine Learning | Model Monitoring | Observability | PEFT | Privacy | Prompt engineering | PyTorch | Python | REST APIs | Retraining | Retrieval-Augmented Generation | Scikit-learn | Scrum | Security | Semantic Kernel | TensorFlow | Version control
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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