Applied AI / ML Engineer
Tasks
- Apply DevOps and MLOps practices
- Apply prompting and context engineering
- Build AI native tools and workflows
- Build applied AI and analytics using Python and SQL
- Build embeddings vector search and RAG systems
- Develop LLM agent workflows and skills
- Document AI approaches and evaluations
- Drive AWS automation and infrastructure as code
- Evaluate AI output quality reliability cost and usability
- Implement reusable AI capabilities
- Implement summarization classification and sentiment workflows
- Monitor log and control AWS costs
- Move prototypes to production
- Process and enrich unstructured text
- Review and improve AI-generated code
- Stand up deploy and operate AWS services
- Use Git for version control
Perks/Benefits
Skills/Tech-stack
API | AWS EC2 | AWS ECS | AWS IAM | AWS Lambda | AWS S3 | Amazon CloudWatch | Claude Code | DevOps | Docker | Embeddings | Git | Infrastructure as Code | JSON | LLM | Litellm | MCP | MLOps | OpenWebUI | Prompt engineering | Python | RAG | SQL | Structured outputs | Vector Search | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Roles
AI | AI Engineer | Applied AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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