Principal AI Engineer
Tasks
- Build RAG pipelines
- Build automation connectors and scripts
- Design and deploy generative AI systems
- Develop APIs microservices and event driven architectures
- Document solution architectures and operating procedures
- Evaluate GenAI tools and vendor options
- Implement agent workflows
- Implement vector databases and retrieval strategies
- Ingest privileged data securely
- Integrate AI services with CRM and case management
- Own MLOps CI CD model monitoring and rollback
- Run model evaluation and A B testing
- Train and fine-tune large language models
- Troubleshoot pilots and ensure production readiness
Perks/Benefits
- 401k
- Dental insurance
- Health insurance
- Paid Holidays
- Paid time off
- Profit sharing
- Telehealth
- Vision insurance
- Wellness programs
Skills/Tech-stack
A/B | A/B Testing | API Integration | AWS | B testing | CI/CD | CloudFormation | Docker | ECS | EKS | Event Driven | Event-driven architecture | Fine Tuning | GCP | GKE | GRPC | GraphQL | Hugging Face | Hugging Face Transformers | Human Feedback | Infrastructure as Code | Keras | Langchain | Learning from Human Feedback | Llamaindex | MLOps | Microservices | Model Monitoring | PyTorch | Python | RAG | REST APIs | Reinforcement Learning | Reinforcement Learning from Human Feedback | Retrieval-Augmented Generation | Rollback strategies | TensorFlow | Terraform | Vector Databases | “as-code”
Education
Associate Degree | Bachelor of Engineering | Bachelor of Science | Master of Science
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