AI ML Engineer
Tasks
- Build ETL ELT pipelines with testing and backfills
- Build batch and real time inference services
- Build end to end ML pipelines for features training and packaging
- Design cost-efficient data architectures
- Design data contracts
- Enforce data quality gates and schema evolution
- Evaluate models with calibration fairness and cost sensitive metrics
- Implement alerting autoscaling and incident runbooks
- Implement model versioning lineage and experiment tracking
- Implement privacy by design including PII tokenization and secrets management
- Integrate inference with message buses and vector databases
- Integrate models via APIs with fallbacks
- Manage rollbacks and canary releases
- Monitor schema data drift and performance regression
- Optimize inference for latency and throughput
- Produce model documentation and decision logs
- Run A B and counterfactual experiments with guardrails
- Select model architectures with success metrics
- Translate business problems into ML formulations
- Tune hyperparameters and optimize reproducibility
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | API | AWS | Apache Spark | Azure | B testing | Batch inference | CI/CD | Calibration | Cloud Architecture | Cloud platform | Counterfactual evaluation | Data Contracts | Data Drift | Data Governance | Data Quality | Databricks | Docker | ELT | ETL | Experiment tracking | Fairness | Google Cloud | Google Cloud Platform | Knowledge Distillation | Kubernetes | MLflow | Machine Learning | Message Queues | Mixed Precision | Model Monitoring | Model versioning | PII Tokenization | PyTorch | Python | Quantization | Real Time | Real-time Inference | Schema evolution | Scikit-learn | Secrets management | TensorFlow | Vector Databases
Education
Roles
AI | AI/ML Engineer | Engineer | Learning Engineer | ML Engineer | Machine Learning Engineer
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