Machine Learning Systems Engineer
Tasks
- Architect and build scalable backend services
- Build event driven media processing workflows
- Build vector search and semantic retrieval systems
- Deploy and operate systems on cloud platforms
- Design media ingestion and processing pipelines
- Ensure observability and incident response
- Evaluate and benchmark models and rollout upgrades
- Implement asynchronous processing patterns
- Integrate AI and ML services into backend workflows
- Mentor engineers and guide technical architecture
- Monitor system and model performance in production
- Optimize model inference for latency throughput reliability cost
- Own backend components end-to-end
- Serve and deploy ML models for real time and batch
Perks/Benefits
Skills/Tech-stack
AWS | Amazon SQS | Asynchronous processing | Audio analysis | Auto Scaling | Azure | Batch Processing | Caching | Cloud PubSub | Cloud platform | Database Design | Deep learning | Distributed Systems | Elasticsearch | Embeddings | Event Driven | Event-driven architecture | Faiss | GPU Utilization | Google Cloud | Google Cloud Platform | Google Cloud PubSub | Hugging Face | Hugging Face Transformers | Image analysis | Inference Optimization | Kafka | Latency optimization | Machine Learning | Message Queue | Microservices | Model Distillation | NoSQL | Node.js | OCR | PGVector | Pinecone | Prompt Optimization | PubSub | PyTorch | Python | Qdrant | Quantization | REST API | SQL | Scene detection | Semantic Search | Speech-to-Text | System design | TensorFlow | Throughput Optimization | Vector Search | Video Analysis | Weaviate
Education
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