Machine Learning Systems Engineer
Tasks
- Apply model optimization techniques including quantization distillation and caching
- Architect scalable backend services for media intelligence platform
- Build high throughput media ingestion and processing pipelines
- Build retrieval workflows for semantic search and similarity matching
- Deploy and operate systems on AWS GCP or Azure
- Design and maintain vector search and indexing systems
- Design data models and storage patterns for media workflows
- Ensure reliability with logging metrics tracing alerting and runbooks
- Implement asynchronous processing with retries idempotency and dead letter queues
- Implement distributed event driven workflows with queues
- Integrate AI ML services into backend model workflows
- Lead metadata extraction transcription and embedding generation workflows
- Mentor engineers and review designs
- Monitor production performance and cost per processed item
- Optimize model inference latency throughput reliability and cost
- Own backend components end-to-end
- Use model serving systems for batching concurrency and autoscaling
Perks/Benefits
Skills/Tech-stack
AWS | Amazon SageMaker | Asynchronous processing | Audio analysis | Autoscaling | Azure | Backend architecture | Batching | Caching | Cloud Pub/Sub | Data Modeling | Dead Letter | Dead Letter Queue | Distributed Systems | Elastic Vectors | Embeddings | Event Driven | Event-driven architecture | Faiss | GPU Utilization | Google Cloud | Google Cloud Pub/Sub | Hugging Face | Hugging Face Transformers | Idempotency | Image Processing | Kafka | Microservices | Model Distillation | NVIDIA Triton | NoSQL | Node.js | OCR | PGVector | Pinecone | Prompt Optimization | Pub/Sub | PyTorch | Python | Qdrant | Quantization | REST API | SQL | SQS | Semantic Search | Similarity Search | Speech-to-Text | System design | TGI | TensorFlow | VLLM | Vector Search | Vertex AI | Video Analysis | Weaviate
Education
N/A
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