Senior AI Engineer
Tasks
- Apply MLOps and LLMOps best practices
- Build LLM powered chatbots
- Build ML and GenAI pipelines
- Build RAG systems
- Build classification systems
- Build fraud-detection systems
- Build information retrieval systems
- Deploy GenAI solutions end to end
- Deploy and operate AI systems on cloud
- Design LLM based applications
- Develop production APIs and services
- Develop recommendation and prediction systems
- Implement deployment monitoring and alerting
- Implement embeddings and vector search
- Implement evaluation pipelines
- Implement model orchestration
- Improve existing ML and AI systems
- Integrate LLMs with internal data sources
- Integrate ML and LLM capabilities
- Write test debug Python code
Perks/Benefits
- 25 vacation days per year
- Discounts at local stores
- Free LinkedIn Learning
- Gym membership
- Hybrid work environment
- Personal development budget
- Private health insurance
- Profit Growth Scheme
Skills/Tech-stack
APIs | AWS | Artificial Intelligence | CI/CD | Cloud platform | Docker | Embeddings | Experiment tracking | GenAI | Google Cloud | Google Cloud Platform | Hugging Face | Infrastructure as Code | LLMOps | Langchain | Language Models | Large Language Models | Llamaindex | MLOps | Machine Learning | Model Deployment | Model Evaluation | Model Monitoring | Observability | OpenAI API | Python | Retrieval-Augmented Generation | Vector Databases | Vector Search | “as-code”
Education
N/A
Roles
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