AI/ML Engineer
Tasks
- Architect RAG pipelines with vector search and hybrid retrieval
- Architect authentication and authorization for AI services
- Author technical design documentation and architecture specs
- Build data ETL pipelines for feature engineering and knowledge base ingestion
- Build production microservices and APIs for AI agent orchestration
- Collaborate with product platform security and operations to ship AI integrations
- Deploy monitor and maintain ML models and AI agent endpoints
- Design AI agent security architectures
- Design build and deploy production AI agents and multi agent orchestration systems
- Develop evaluation and monitoring frameworks for AI systems
- Integrate third party AI platforms and LLM providers
- Lead architecture reviews and implementation roadmaps
Perks/Benefits
- 401k match
- Disability benefits
- Fertility healthcare
- Flexible work from home
- HSA match
- Medical/Dental/Vision
- Mental health resources
- Paid Holidays
- Paid life insurance
- Paid parental and caregiving leave
- Professional development stipends
- Unlimited PTO
Skills/Tech-stack
AWS SageMaker | Agent systems | Agentic Architectures | Airflow | Alerting | Apache Spark | Authentication | Authorization | CI/CD | Cloud deployment | Data ETL | Databricks | Delegated Authorization | FastAPI | Feature Engineering | Fine Tuning | Flask | Function Calling | GitHub Actions | Google Vertex | Google Vertex AI | Hybrid retrieval | JavaScript | Jenkins | Kubernetes | Langchain | Langgraph | Language Models | Large Language Models | Llamaindex | Microservices | Model Monitoring | Multi-Agent | Multi-Agent Systems | Node.js | OAuth | Prompt engineering | Python | REST APIs | Retrieval-Augmented Generation | Terraform | Testing | Token based | Token-based authentication | Tool use | TypeScript | Vector Search | Vertex AI
Education
N/A
Roles
AI | AI/ML Engineer | Engineer | ML Engineer
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