Ai Engineer
Tasks
- Apply testing version control and code review
- Build APIs and services for AI models
- Build agentic workflows and AI agents
- Build and deploy Generative AI systems
- Build and operate ML pipelines
- Containerize applications with Docker
- Create prompt engineering workflows
- Deploy and monitor model serving
- Develop LLM-powered applications
- Develop and maintain data pipelines
- Evaluate model outputs
- Fine-tune foundation models
- Implement MLOps workflows
- Implement retrieval-augmented generation
- Implement vector databases and embeddings
- Mentor junior engineers
- Present technical findings to stakeholders
- Set up CI/CD pipelines
- Support workshops and proofs of concept
- Translate business needs into AI solutions
Perks/Benefits
- 401k matching
- Cross training
- Dental insurance
- Hybrid work
- Medical insurance
- Paid Holidays
- Paid sick time
- Paid vacation
- Safety focused culture
- Vision insurance
Skills/Tech-stack
AWS Bedrock | AWS SageMaker | Agent Framework | Azure Machine Learning | Azure OpenAI | CI/CD | Code review | Data Pipelines | Docker | ETL | Embeddings | Experimentation | Fine Tuning | Generative AI | Google Cloud | Google Cloud Vertex | Google Cloud Vertex AI | Langchain | Language Models | Large Language Models | MLOps | Machine Learning | Microsoft Agent | Microsoft Agent Framework | Model Serving | Palantir AIP | Pandas | Power Automate | Prompt engineering | Python | Retrieval-Augmented Generation | SQL | Scikit-learn | Semantic Kernel | Snowflake | Statistical modeling | Testing | Vector Databases | Version control | Vertex AI
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI Engineer | Data Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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