Senior Machine Learning Engineer
Tasks
- Architect CI CD systems for ML models
- Automate resilience and disaster recovery planning
- Define observability standards for ML systems
- Design scalable cloud infrastructure for ML workloads
- Implement low latency real time model inference
- Improve platform usability for ML teams
- Lead incident response and root cause analysis
- Maintain KV store and vector databases
- Monitor model performance and drift
- Operate production-grade ML infrastructure
- Streamline end to end ML lifecycle
Perks/Benefits
- Dental benefits
- Healthcare benefits
- Hybrid work schedule
- On-call rotation
- Remote work on Fridays
- Retirement plan
- Vision benefits
Skills/Tech-stack
AWS | Aerospike | Apache Airflow | Apache Flink | Apache Spark | CI/CD | Chronon | Datadog | Datastore | DevOps | Docker | EKS | GCP | GKE | GPU | GitLab Runner | Grafana | Infrastructure as Code | Java | Jenkins | KV store | Kafka | Kubernetes | MLOps | MLflow | Machine Learning | NoSQL | Prometheus | Python | Ray | Reliability Engineering | Scala | Site Reliability | Site Reliability Engineering | TPU | Terraform | Vector Database | “as-code”
Education
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