Machine Learning Systems Engineer
Tasks
- Architect scalable backend services
- Build high throughput media ingestion pipelines
- Deploy and manage model-serving systems
- Deploy and operate systems on cloud infrastructure
- Design data models and storage patterns
- Design vector search indexing and retrieval workflows
- Ensure observability with logging metrics tracing alerts dashboards runbooks
- Evaluate and optimize models with quantization distillation batching caching
- Implement asynchronous processing patterns with retries idempotency dead letter backpressure
- Implement event driven workflows with queues and pub sub
- Integrate AI ML services into backend workflows
- Lead metadata extraction and content analysis workflows
- Mentor engineers and lead technical discussions
- Monitor production model and system performance
- Optimize inference for latency throughput reliability cost
- Own backend components end-to-end
Perks/Benefits
Skills/Tech-stack
AWS | Amazon SageMaker | Asynchronous processing | Autoscaling | Backpressure | Batching | Caching | Cloud platform | Data Modeling | Dead Letter | Dead Letter Queue | Distributed Systems | Elastic Vectors | Embeddings | Event Driven | Event-driven architecture | Faiss | GPU Utilization | Google Cloud | Google Cloud Platform | Hugging Face | Hugging Face Transformers | Idempotency | Microservices | Microsoft Azure | Model Distillation | Model routing | NVIDIA Triton | NoSQL | Node.js | OCR | PGVector | Pinecone | Prompt Optimization | PyTorch | Python | Qdrant | Quantization | REST API | SQL | Semantic Search | Similarity Search | Speech-to-Text | System design | TGI | TensorFlow | VLLM | Vector Search | Vertex AI | Weaviate
Education
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