Senior Machine Learning Engineer
Tasks
- Assess model risk and governance strategies
- Build NLP ranking and recommendation systems
- Build forecasting and semantic retrieval solutions
- Build training workflows and model lifecycle management
- Define offline and online evaluation benchmarks
- Develop RAG pipelines and evaluation harnesses
- Develop model serving infrastructure
- Drive online experimentation
- Guide model selection and distributed training
- Implement drift detection and retraining strategies
- Implement observability and monitoring
- Implement safety controls and guardrails
- Improve model compression and latency
- Lead design and delivery of production ML systems
- Mentor engineers and develop reusable patterns
- Optimize GPU utilization and inference
- Optimize prompt and retrieval performance
- Own architecture and implementation of data pipelines
- Own feature store implementation and usage
Perks/Benefits
- Continuous learning opportunities
- Flexible working models
- Great benefits
- Health and well-being
- Hybrid work model
- Skill growth
Skills/Tech-stack
Agent workflows | Automated Validation | CI/CD | Deep learning | Distributed Training | Distributed inference | Drift Detection | Embeddings | Event Driven | Event-driven architecture | Experiment tracking | Feature Stores | Fine Tuning | Forecasting | GPU Optimization | Go | Guardrails | Information Retrieval | Infrastructure as Code | Java | Language Models | Language Processing | Large Language Models | MLOps | Machine Learning | Model Compression | Model Registry | Model Serving | Monitoring | Natural Language | Natural Language Processing | Observability | Offline Benchmarking | Online Experimentation | Prompt engineering | PyTorch | Python | RAG | Ranking | Recommendation Systems | Scikit-learn | Semantic Retrieval | Streaming Data | TensorFlow | Vector Databases | “as-code”
Education
N/A
Regions
Countries
States
Cities
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