Lead AI Engineer
Tasks
- Apply ML lifecycle best practices for data training and evaluation
- Build production ML pipelines
- Build reusable accelerators and reference architectures
- Coordinate delivery across engineering data science and customer teams
- Deliver agentic AI systems and LLM applications
- Deploy and monitor AI models in production
- Design model architecture and deployment approach
- Ensure governance for AI and ML
- Lead end to end delivery of AI and ML solutions
- Oversee predictive ML and GenAI implementations
- Review ML models and GenAI systems
- Translate business problems into technical AI solutions
Perks/Benefits
Skills/Tech-stack
APIs | Agentic Workflows | Backend Services | CI/CD | Classification | Cloud Platforms | Clustering | Data Preparation | Debugging | Feature Engineering | Generative AI | Interpretability | LLMOps | Language Models | Large Language Models | MLOps | Machine Learning | Model Evaluation | Model Governance | Model Monitoring | Predictive Modeling | Prompt engineering | Python | Retrieval-Augmented Generation | System design | Testing | Time Series | Version control
Education
N/A
Roles
AI Engineer | Engineer | Lead | Lead AI Engineer | Learning Engineer | Machine Learning Engineer
Regions
Countries
States
Cities
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