Machine Learning Systems Engineer
Tasks
- Architect scalable backend services
- Build event driven workflows with queues and pub sub
- Build semantic search retrieval and duplicate detection workflows
- Deploy and operate systems on cloud
- Design media ingestion and processing pipelines
- Design vector search and indexing systems
- Ensure reliability with logging metrics tracing alerting and runbooks
- Evaluate model upgrades and benchmark model quality and latency
- Implement asynchronous processing with retries idempotency and dead letter queues
- Integrate AI and ML services into backend workflows
- Manage model serving and GPU utilization
- Monitor performance and cost in production
- Optimize inference for latency throughput reliability and cost
- Optimize metadata extraction scene detection transcription and embedding generation
- Own backend components end-to-end
Perks/Benefits
Skills/Tech-stack
AWS | Amazon SQS | Amazon SageMaker | Autoscaling | Batching | Caching | Distributed Systems | Elastic Vectors | Embedding Generation | Faiss | GPU Utilization | Google Cloud | Google Pub/Sub | Hugging Face | Hugging Face Transformers | Kafka | Logging | Metrics | Microservices | Microsoft Azure | Model Distillation | Multimodal Learning | NVIDIA Triton | NoSQL | Node.js | OCR | Observability | PGVector | Pinecone | Prompt Optimization | Pub/Sub | PyTorch | Python | Qdrant | Quantization | REST API | Retrieval-Augmented Generation | SQL | Semantic Search | Speech-to-Text | System design | TGI | TensorFlow | Tracing | VLLM | Vector Search | Vertex AI | Weaviate
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