AI/ML Engineering Manager
Tasks
- Apply LLM fine tuning
- Architect agentic AI systems
- Build and deploy RAG systems
- Build data engineering pipelines
- Build fault tolerant automation pipelines
- Build retraining pipelines
- Design and ship production agentic AI systems
- Detect model drift
- Develop and deploy predictive ML models
- Diagnose data quality issues
- Evaluate AI outputs
- Evaluate build vs buy tradeoffs
- Implement MLOps infrastructure
- Implement orchestration logic and state management
- Implement production monitoring
- Implement prompt engineering strategies
- Implement retry logic and reliability for long running tasks
- Implement vector retrieval pipelines
- Lead AI ML engineering team
- Map workflows and identify automation opportunities
- Own AI/ML technical roadmap
- Perform feature engineering
- Track experiments and manage model versions
Perks/Benefits
- Employee Discount Purchase Program
- Employee stock purchase program
- Holiday pay
- Medical, dental, vision benefits
- Paid parental leave
- Paid time off
- Pet insurance
- Tuition assistance
Skills/Tech-stack
AI Foundry | AWS Bedrock | Agent Orchestration | Airflow | Apache Spark | Azure AI | Azure AI Foundry | CI/CD | CrewAI | DBT | Dagster | Data Pipelines | Docker | Drift Detection | Experiment tracking | Feature Engineering | Fine Tuning | Human-in-the-loop | Kubernetes | LLM Fine-tuning | Langchain | Langgraph | LoRA | MLOps | MLflow | Memory Management | Microsoft Fabric | Model Drift | Model Drift Detection | Model versioning | Multi-Agent | Multi-agent orchestration | PEFT | Prompt engineering | Python | RAG | SQL | Temporal | The Loop | Vector Databases | Weights and Biases | Workflow Orchestration
Education
N/A
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