AI Engineer
Tasks
- Apply AI and advanced analytics to healthcare data use cases
- Build Retrieval Augmented Generation systems
- Build scalable secure reliable and compliant AI solutions
- Build semantic search systems
- Create AI deployment pipelines for monitoring governance and optimization
- Create reusable AI frameworks and APIs
- Design build deploy maintain production AI and machine learning solutions
- Develop data pipelines for AI ready data ecosystems
- Develop decision support and forecasting models
- Develop enterprise generative AI and agentic AI applications
- Develop intelligent workflow automation
- Evaluate validate and benchmark AI models
- Implement AI governance responsible AI practices and model risk management
- Implement AI orchestration frameworks
- Implement LLM application development
- Implement automated testing, monitoring, and observability
- Perform prompt engineering
- Set up CI/CD pipelines
- Translate business problems into structured AI solutions
Perks/Benefits
- N/A
Skills/Tech-stack
API Development | Agent systems | Agentic AI | Apache Spark | CI/CD | Databricks | Docker | Generative AI | Git | Healthcare Analytics | Kubernetes | LLMOps | Language Models | Large Language Models | MLOps | Machine Learning | Model Governance | Monitoring | Multi-Agent | Multi-Agent Systems | Observability | Prompt engineering | PySpark | Python | Retrieval-Augmented Generation | SQL | Semantic Search | Snowflake | Workflow Orchestration
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer | Senior AI Engineer
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