Lead Engineer, Machine Learning
Tasks
- Architect production AI ML systems
- Build LLMOps infrastructure
- Build RAG systems
- Build agentic pipelines
- Build data processing pipelines
- Collaborate with product, engineering, and data science
- Define AI ML capability scope and plans
- Deploy machine learning models
- Design API services
- Develop model validation and evaluation
- Develop monitoring observability and logging
- Engineer batch model serving
- Engineer real time model serving
- Implement CI/CD integration
- Implement automated testing
- Maintain test coverage
- Mentor ML engineers
- Mentor data scientists
- Perform code reviews
- Perform feature engineering
- Perform performance testing
- Plan delivery epics and dependencies
- Promote ML engineering best practices
- Resolve engineering blockers and delays
- Run A B experimentation
- Write code in continuous release environment
Perks/Benefits
- 30 percent merchandise discount
- 401k match
- Dental insurance
- Disability insurance
- Donation matching
- FSA
- Financial coaching resources
- Flexible work schedule
- Free mental health resources
- Free product or gratis
- HSA
- Health insurance
- Life insurance
- PTO
- Tuition reimbursement
- Vision insurance
- Volunteer opportunities
Skills/Tech-stack
API Design | AWS | Azure | CI/CD | Data Processing | Databricks | Distributed Systems | Docker | Feature Engineering | Feature Store | Fine Tuning | GCP | Kubernetes | LLMOps | Langchain | Langgraph | Language Models | Large Language Models | MLOps | MLflow | Model Evaluation | Model Serving | Model Training | Observability | Prompt engineering | Python | RAG | Recommendation Systems | Reinforcement Learning | Supervised Learning | Testing | Unsupervised Learning | Vector Databases
Education
N/A
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