MLOps / LLMOps Engineer
Tasks
- Apply tagging policies for ML cost tracking and optimization
- Automate deployments across DEV QA PROD
- Build CI/CD pipelines for ML workflows
- Build and optimize LLM pipelines for RAG and inference
- Create runbooks disaster recovery procedures and operational documentation
- Deploy and manage ML and GenAI solutions
- Develop unit and integration tests for data pipelines and ML models
- Enforce data residency governance compliance policies
- Ensure secure handling of sensitive data PII detection classification obfuscation
- Implement alerting incident response and automated ticketing
- Implement batch and real time inference pipelines with SLAs
- Implement data quality checks schema validation data contracts
- Implement model governance lineage tracking access control
- Maintain data to model lineage to serving endpoints
- Monitor pipeline health model performance drift and reliability
- Operationalize model training evaluation packaging deployment
- Support budget monitoring cost optimization and resource management
- Track LLM performance metrics latency hallucination rates API costs
Perks/Benefits
- N/A
Skills/Tech-stack
Alerting | Batch inference | CI/CD | Caching | Cost Optimization | Data Contracts | Data Processing | Data Quality | Data Residency | Databricks | Delta Lake | Disaster Recovery | Distributed data | Distributed data processing | Encryption | FinOps | GitHub Actions | Governance | Lineage | MLflow | Model Serving | Monitoring | Observability | Prompt engineering | Python | Quantization | RAG | RBAC | Real Time | Real-time Inference | SQL | Schema Validation | Unity Catalog | Vector Databases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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