AI/Machine Learning Engineer
Summary
Design, build, and deploy AI/ML models and services, including generative AI and RAG systems, while ensuring responsible AI practices and robust MLOps pipelines.
- Apply responsible AI principles fairness transparency explainability reliability human oversight
- Build AI model deployment pipelines and model lifecycle
- Build and maintain AI services APIs microservices
- Conduct technical reviews and implementation planning
- Design and develop data pipelines for training and inference
- Design develop test deploy AI ML models
- Develop prompt engineering evaluation optimization
- Ensure alignment with AI governance security privacy and data protection
- Implement Retrieval-Augmented Generation architectures
- Implement automated testing deployment monitoring model versioning release management
- Implement generative AI agentic AI solutions
- Implement observability and monitoring frameworks for AI workloads
- Integrate AI solutions with enterprise applications cloud APIs databases data warehouses
- Investigate production issues perform root cause analysis implement corrective actions
- Monitor model performance accuracy reliability cost and stability
- Perform data preparation feature engineering and model operationalization
- Support AI risk assessments security reviews compliance documentation
- Support production deployment aligned to enterprise architecture
- Tune and optimize models for performance quality and usability