Staff Machine Learning Ops Engineer
Tasks
- Build genai and llm gateway services
- Create prompt evaluation and experimentation frameworks
- Define ML platform vision and roadmap
- Design cloud infrastructure for distributed training and inference
- Design platform abstractions and self service tooling
- Establish ML observability standards
- Identify and eliminate ml research to production bottlenecks
- Implement reliable model serving patterns
- Implement vector retrieval infrastructure
- Lead ML lifecycle architecture
- Mentor engineers and raise engineering standards
- Optimize latency for model inference
- Set ci cd direction for ml
Perks/Benefits
- Equity
- Health insurance
- Learning and development budget
- Leave allowance
- Mental health support
- Time off for self-development
Skills/Tech-stack
AWS | Autoscaling | CI/CD | Cost Optimization | Data Quality | Deployment | Distributed Computing | Drift Detection | Feature Engineering | GCP | GPU Computing | GenAI | Governance | Infrastructure as Code | Kubernetes | LLM | MLOps | Machine Learning | Model Serving | Model versioning | Monitoring | Observability | Prompt engineering | Prompt evaluation | Retraining | Retrieval-Augmented Generation | Rollout strategies | Security | Training | Vector Databases | Workload isolation | “as-code”
Education
N/A
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