Machine Learning Systems Engineer
Tasks
- Architect scalable backend services for media intelligence platform
- Build event driven workflows for media processing
- Deploy and operate cloud infrastructure and observability tools
- Deploy and operate model serving systems and manage GPU utilization
- Design media ingestion and processing pipelines
- Design vector search indexing and retrieval workflows
- Develop metadata extraction content analysis and transcription pipelines
- Implement asynchronous processing with retries idempotency and dead letter queues
- Integrate AI ML services into backend workflows
- Mentor engineers and review designs
- Monitor system performance and maintain operational reliability
- Optimize model inference for latency throughput and reliability
- Own backend components end-to-end
Perks/Benefits
Skills/Tech-stack
AWS | Amazon SageMaker | Asynchronous processing | Azure | Backpressure | Batching | Caching | Data Modeling | Dead Letter | Dead Letter Queue | Distributed Systems | Elastic Vectors | Elasticsearch | Embedding Generation | Embeddings | Event Driven | Event-driven architecture | Faiss | GCP | Hugging Face | Hugging Face Transformers | Idempotency | Kafka | Message Queues | Microservices | Model Distillation | Model Serving | Multimodal AI | NVIDIA Triton | NoSQL | Node.js | OCR | PGVector | Pinecone | Prompt Optimization | Pub/Sub | PubSub | PyTorch | Python | Qdrant | Quantization | REST APIs | Retries | Routing | SQL | SQS | Semantic Search | Similarity Search | Speech-to-Text | System design | TGI | TensorFlow | VLLM | Vector Search | Vertex AI | Weaviate
Education
N/A
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