AI Engineer
Tasks
- Apply LLMOps and MLOps best practices
- Build ML and GenAI data ingestion pipelines
- Build chatbot and information retrieval systems
- Build embeddings and vector search pipelines
- Create model evaluation and testing pipelines
- Deploy AI systems on cloud infrastructure
- Design LLM based applications
- Develop RAG systems
- Develop production APIs and services
- Implement summarization and classification pipelines
- Improve ML AI systems for recommendations and predictions
- Integrate LLMs with internal data sources
- Monitor AI systems and set up alerting
- Orchestrate and deploy ML models
- Write testable Python code
Perks/Benefits
- 25 vacation days per year
- 5 fully paid sick days
- 5 special occasion days
- Additional vacation days option
- Discounts at local stores
- Great equipment
- Gym membership
- Hybrid work environment
- International team
- LinkedIn Learning
- Personal development budget
- Team activities and perks
Skills/Tech-stack
APIs | AWS | Alerting | CI/CD | Cloud platform | Containers | Embeddings | Experiment tracking | Generative AI | Google Cloud | Google Cloud Platform | Hugging Face | Infrastructure as Code | LLMOps | Langchain | Language Models | Large Language Models | Llamaindex | MLOps | Model Evaluation | Monitoring | Observability | OpenAI API | Orchestration | Python | Retrieval-Augmented Generation | Vector Databases | Vector Search | “as-code”
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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