AI ML Engineer
Tasks
- Build end-to-end ML pipelines
- Build real time and batch inference services
- Create alerting and autoscaling policies
- Design A B and counterfactual experiments
- Design cost-efficient data architectures
- Design data contracts and data testing
- Enforce data quality gates and schema evolution
- Evaluate models beyond accuracy
- Implement ETL/ELT pipelines
- Implement model versioning and experiment tracking
- Implement privacy by design PII handling tokenization and secure secrets
- Integrate inference with message buses and vector databases
- Integrate models via APIs and SDKs with fallbacks
- Manage rollbacks and canary releases
- Monitor data drift schema checks and performance regression
- Optimize inference for latency and throughput
- Package models as reproducible artifacts
- Perform feature extraction training and hyperparameter tuning
- Produce model cards and decision logs
- Select ML architectures with success metrics
- Translate business problems into ML formulations
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | AWS | Azure | B testing | CI/CD | Cloud Computing | Counterfactual evaluation | Data Contracts | Data Drift | Data Governance | Data Quality | Databricks | Deep learning | Docker | ELT | ETL | Experiment tracking | Google Cloud | Inference services | Knowledge Distillation | Kubernetes | MLflow | Machine Learning | Message bus | Mixed Precision | Model Optimization | Model versioning | PII Tokenization | Privacy Compliance | PyTorch | Python | Quantization | Schema evolution | Scikit-learn | Secrets management | Spark | TensorFlow | Vector Database
Education
Roles
AI | AI/ML Engineer | Engineer | Learning Engineer | ML Engineer | Machine Learning Engineer
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