Senior Machine Learning Engineer
Tasks
- Build real time and batch inference services
- Deploy ML models to production
- Design and prototype ML powered products
- Ensure reproducibility
- Identify and pilot GenAI and LLM use cases
- Implement CI/CD for models
- Integrate ML decision services into game systems
- Manage model registry and feature stores
- Mentor engineers and raise engineering standards
- Operate low latency prediction services
- Own ML Ops practice
- Plan and execute ML application launches
- Set up monitoring and drift detection
- Translate concepts into secure scalable measurable solutions
Perks/Benefits
Skills/Tech-stack
AWS | AWS CloudFormation | Amazon SageMaker | Apache Airflow | Apache Kafka | Apache Spark | Azure | CI/CD | Containers | Databricks | Databricks Delta | Delta Lake | Drift Detection | Feature Store | GCP | Infrastructure as Code | Kinesis | Kubeflow | Kubernetes | Learning operations | MLflow | Machine Learning | Machine Learning Operations | Microservices | Model Monitoring | Model Registry | NoSQL | PyTorch | Python | RAG | Real Time | Real-time Inference | Relational databases | Reproducibility | Scikit-learn | Serverless | Spark MLlib | Spark Streaming | TensorFlow | Terraform | Unity | Unreal Engine | Vector Databases | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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